Awesome Neural Architecture Search Papers

（中文）

We would like to maintain a complete list of NAS-related papers and provide a guide for some of the papers that have received wide interest.

2020 (283)
2019 (261)
2018 (72)
2017 (21)
2016 (10)
1988~2015 (12)

Tasks

Medical
Image Segmentation / Object Detection / Semantic Segmentation / Image Recognition
Remote Sensing
Scene Text Recognition
Autonomous Driving
Image Translator / Image Denoising
Language Modeling / Speech Recognition / NLP
Model Compression / Multi-objective Search / Binary Networks
GAN / Unsupervised
Video Models
GNN
CTR
Time Series
Federated Learning / Private Inference
3D Deep Learning
Multimodal Learning
Imitation Learning
Meta-learning
Distributed System
Benchmark

Medical

Year	Title	Code
2020	Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020) accepted at Revue d’Intelligence Artificielle
2020	Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020) accepted at MICCAI 2020
2020	Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020) accepted at MICCAI 2020
2020	Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020) accepted at Computerized Medical Imaging and Graphics
2020	AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020) accepted at Neural Networks
2020	AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) accepted at IEEE Access
2020	Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020) accepted at IEEE ISBI
2020	Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020) accepted at IEEE Access
2019	Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019) accepted at MICCAI’19
2019	Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) accepted at MICCAI’19
2019	Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) accepted at MICCAI’19
2019	Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019) accepted at MICCAI’19
2019	Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019) accepted at SPIE Medical Imaging’20
2019	AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.) accepted at Neurocomputing
2019	NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) accepted at IEEE Access
2020	Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)
2020	MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)
2020	Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020)
2020	Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020)
2020	Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020)
2020	AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020)
2020	Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020)
2020	ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020)
2020	Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020)	Github
2020	ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)
2019	C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019)
2019	SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019)
2019	V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019)
2019	Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019)
2018	Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018)

Image_Segmentation

Year	Title	Code
2020	AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020) accepted at Neural Networks
2020	AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) accepted at IEEE Access
2020	Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR’20	Github
2019	Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019) accepted at MICCAI’19
2019	Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) accepted at MICCAI’19
2019	Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) accepted at MICCAI’19
2019	Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019) accepted at MICCAI’19
2019	Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019) accepted at SPIE Medical Imaging’20
2019	AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.) accepted at Neurocomputing
2019	NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) accepted at IEEE Access
2019	Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019) accepted at CVPR’19	Github
2018	Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018) accepted at CVPR’19
2020	Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)
2020	MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)
2020	DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020)
2020	ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)
2019	C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019)
2019	SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019)
2019	Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019)
2019	SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019)
2019	V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019)
2019	Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019)
2019	Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019)
2018	Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018)

Model_Compression

Year	Title	Code
2020	Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) accepted at IEEE Geoscience and Remote Sensing Letters
2020	Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020) accepted at ECCV 2020
2020	Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020) accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System
2020	Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020) accepted at ECCV 2020	Github
2020	CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) accepted at IJCAI 2020
2020	Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020) accepted at CVPR 2020	Github
2020	NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee) accepted at CVPR 2020	Github
2020	Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020) accepted at accetped at ICASSP 2020
2020	Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020) accepted at On-device Intelligence Workshop at MLSys’20
2020	Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020) accepted at ICMLA’19
2020	Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020) accepted at CogMI’20
2020	Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020) accepted at DAC’20
2020	Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020) accepted at DAC’20
2020	FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020) accepted at FPT’20
2020	Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020) accepted at ECAI'20
2020	HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020)
2020	Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020)
2020	CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020)
2020	MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)
2020	GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)	Github
2020	Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020)
2020	AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)

Multi-objective_Search

Year	Title	Code
2020	CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) accepted at ECCV 2020	Github
2020	NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020) accepted at ECCV 2020	Github
2020	Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020) accepted at IEEE System Journal
2020	Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020) accepted at IJCAI 2020
2020	Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020) accepted at accpeted at ICME 2020
2020	Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020) accepted at CVPR 2020 workshop
2020	MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020) accepted at CVPR 2020	Github
2020	APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020) accepted at CVPR 2020	Github
2020	Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020) accepted at IEEE Internet of Things
2020	FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020) accepted at ASP-DAC 2020
2020	DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020) accepted at Microprocessors and Microsystems
2020	Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020)	Github
2020	You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020)
2020	FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020)	Github

Object_Detection

Year	Title	Code
2020	Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020) accepted at ECCV 2020
2020	FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR 2020	Github
2020	SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020) accepted at CVPR 2020
2020	Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020) accepted at IEEE Journal of Selected Topics in Signal Processing
2020	Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020) accepted at CVPR 2020	Github
2020	Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR’20	Github
2019	Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019) accepted at ICCV’19
2019	DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019) accepted at NeurIPS’19	Github
2020	MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)

GAN

Year	Title	Code
2020	Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020) accepted at ECCV 2020	Github
2020	A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020) accepted at GECCO 2020
2020	AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) accepted at ICML 2020	Github
2019	AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019) accepted at ICCV’19	Github
2020	Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020)	Github
2020	AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020)	Github
2020	Conditional Neural Architecture Search(Kao et al. 2020)
2020	GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)	Github

Image_Translator

Year	Title	Code
2020	AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) accepted at ICML 2020	Github
2020	Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020) accepted at CVPR 2020
2020	All in One Bad Weather Removal using Architectural Search(Li et al. 2020) accepted at CVPR 2020
2020	Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020)
2020	Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020)
2020	Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020)

Video_Models

Year	Title	Code
2020	AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020) accepted at ECCV 2020
2020	Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) accepted at WACV 2020
2020	Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020) accepted at 7th ICML Workshop on Automated Machine Learning, 2020
2018	Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018) accepted at ICCV’19
2019	Video Action Recognition via Neural Architecture Searching(Peng et al. 2019)
2019	AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019)

GNN

Year	Title	Code
2020	Graph Neural Architecture Search(Gao et al. 2020) accepted at IJCAI 2020	Github
2020	A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020) accepted at CVPR 2020
2020	Neural Architecture Optimization with Graph VAE(Li et al. 2020)
2020	A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020) accepted at ECCV 2020	Github
2020	Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020)

Unsupervised

Year	Title	Code
2020	Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020) accepted at NTIRE2020 Workshop at CVPR 2020
2020	An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020) accepted at CCWC’20
2020	Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020)
2020	Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020)	Github

Binary_Networks

Year	Title	Code
2020	CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) accepted at IJCAI 2020
2020	DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020) accepted at 1st Workshop on Neural Architecture Search at ICLR 2020
2020	BATS: Binary ArchitecTure Search(Bulat et al. 2020) accepted at ECCV’20	Github
2020	Learning Architectures for Binary Networks(Kim et al. 2020) accepted at ECCV’20

CTR

Year	Title	Code
2020	Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020) accepted at KDD2020
2020	AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020)
2020	Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020)
2020	AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020)

Multimodal_Learning

Year	Title	Code
2020	Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020) accepted at MICCAI 2020
2020	RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020) accepted at Meta-Eval 2020 workshop
2019	MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019) accepted at CVPR’19
2020	Deep Multimodal Neural Architecture Search(Yu et al. 2020)

Federated_Learning

Year	Title	Code
2020	FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020) accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning	Github
2020	Differentially-private Federated Neural Architecture Search(Singh et al. 2020)	Github
2020	Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020)
2020	Neural Architecture Search over Decentralized Data(Xu et al. 2020)

Speech_Recognition

Year	Title	Code
2020	DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020) accepted at INTERSPEECH 2020
2020	Neural Architecture Search for Speech Recognition(Hu et al. 2020)
2020	AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020)	Github

Benchmark

Year	Title	Code
2020	NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020) accepted at ICLR’20
2020	NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020) accepted at ICLR’20	Github
2020	NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)

Remote_Sensing

Year	Title	Code
2020	Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) accepted at IEEE Geoscience and Remote Sensing Letters
2020	RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020) accepted at IEEE Transactions on Geoscience and Remote Sensing
2020	Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010)

3D_Deep_Learning

Year	Title	Code
2020	Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020) accepted at ECCV 2020
2020	Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020) accepted at IEEE Geoscience and Remote Sensing Letters
2020	Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020) accepted at IEEE Access

Scene_Text_Recognition

Year	Title	Code
2020	Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) accepted at WACV’20 workshop
2020	Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020)

NLP

Year	Title	Code
2020	NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)
2020	AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)

Private_Inference

Year	Title	Code
2020	SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020)
2020	CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020)

Imitation_Learning

Year	Title	Code
2020	NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020) accepted at ICASSP 2020
2020	AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020)

Time_Series

Year	Title	Code
2020	Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020) accepted at ijcnn 2020
2020	Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020)

Semantic_Segmentation

Year	Title	Code
2020	Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) accepted at WACV 2020
2020	FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR 2020	Github

Distributed_System

Year	Title	Code
2020	A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020) accepted at CCWC’20
2020	Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020)

Autonomous_Driving

Year	Title	Code
2020	Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020) accepted at ECCV 2020
2020	CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) accepted at ECCV 2020	Github

Meta-learning

Year	Title	Code
2020	CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020) accepted at ECCV 2020
2020	M-NAS: Meta Neural Architecture Search(Wang et al. 2020) accepted at AAAI 2020

Language_Modeling

Year	Title	Code
2020	Searching Better Architectures for Neural Machine Translation(Fan et al. 2020) accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing
2020	Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020) accepted at ACL 2020

Image_Denoising

Year	Title	Code
2020	Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020) accepted at NTIRE2020 Workshop at CVPR 2020
2020	Neural Architecture Search for Deep Image Prior(Ho et al. 2020)

Image_Recognition

Year	Title	Code
2020	On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020) accepted at ICCV 2019	Github
2020	Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) accepted at WACV’20 workshop

2020

Title	Tags	Code
Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification(Dondeti et al. 2020) accepted at Revue d’Intelligence Artificielle	Medical Image Classification NASNet	-
A Unified Approach to Anomaly Detection(Ball et al. 2020) accepted at The Sixth International Conference on Machine Learning	Anomaly Detection AutoEncoder Evoluationary	-
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation(Yuan et al. 2020)	Monaural Singing Voice Separation Evolutionary	-
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap(Xie et al. 2020)	Survey CV	-
Neural Architecture Search in Graph Neural Networks(Nunes and L.Pappa 2020)	Graph Neural Networks Evolutionary RL	-
Anti-Bandit Neural Architecture Search for Model Defense(Chen et al. 2020) accepted at ECCV 2020	Adversarial Defense ABanditNAS	Github
HMCNAS: Neural Architecture Search Using Hidden Markov Chains And Bayesian Optimization(Lopes and Alexandre 2020)	HMCNAS Evolutionary	-
Neural Architecture Search as Sparse Supernet(Wu et al. 2020)	-	-
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution(Tang et al. 2020) accepted at ECCV 2020	3D Deep Learning Autonomous Driving Resource Constraints Evolutionary	-
Growing Efficient Deep Networks by Structured Continuous Sparsification(Yuan et al. 2020)	Network Pruning	-
Lidar Data Classification Based on Automatic Designed CNN(Xie and Chen 2020) accepted at IEEE Geoscience and Remote Sensing Letters	3D Deep Learning Gradient-based	-
Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning(Popescu et al. 2020) accepted at IEEE Access	Human Activity Recognition 3D Deep Learning CV RL	-
Mixed-Precision Quantization for CNN-Based Remote Sensing Scene Classification(Wei et al. 2020) accepted at IEEE Geoscience and Remote Sensing Letters	Remote Sensing Model Compression Mixed-Precision Quantization	-
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound(Huang et al. 2020) accepted at MICCAI 2020	Medical GDAS RL	-
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search(Hu et al. 2020) accepted at ECCV 2020	TF-NAS	Github
Efficient Oct Image Segmentation Using Neural Architecture Search(Gheshlaghi et al. 2020)	Medical Image Segmentation ProxylessNAS	-
SOTERIA: In Search of Efficient Neural Networks for Private Inference(Aggarwal et al. 2020)	Private Inference DARTS	-
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning(Zhao et al. 2020)	Multi-Domain Learning	-
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending(Xu et al. 2020) accepted at ECCV 2020	Autonomous Driving Lane Detection Multi-objective Search Evolutionary	Dataset
Representation Sharing for Fast Object Detector Search and Beyond(Zhou et al .2020) accepted at ECCV 2020	Object Detection	-
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification(Wang et al. 2020) accepted at ECCV 2020	Video Models DARTS	-
MCUNet: Tiny Deep Learning on IoT Devices(Lin et al. 2020)	IoT	-
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization(Yu et al. 2020) accepted at ECCV 2020	Model Compression Mixed Precision Quantization DARTS	-
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search(Lu et al. 2020) accepted at ECCV 2020	Multi-objective Search	Github
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search(Chen et al. 2020) accepted at ECCV 2020	Meta-learning RL	-
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start(Jiang et al. 2020) accepted at IEEE Transactions On Computer-Aided Design of Integrated Circuits and System	Model Compression HotNAS RL	-
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search(Tian et al. 2020) accepted at ECCV 2020	GAN RL	Github
Neural Architecture Search for Speech Recognition(Hu et al. 2020)	Speech Recognition DARTS	-
BRP-NAS: Prediction-based NAS using GCNs(Chau et al .2020)	Predictor-based GCN	-
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes(do Nascimento et al. 2020) accepted at ECCV 2020	Model Compression Bayesian Optimization	Github
One-Shot Neural Architecture Search via Novelty Driven Sampling(Zhang et al. 2020) accepted at IJCAI 2020	Evolutionary Single-path One-shot	-
Neural Architecture Search in A Proxy Validation Loss Landscape(Li et al. 2020) accepted at ICML 2020	Estimation Strategy	-
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs(Zhuo et al. 2020) accepted at IJCAI 2020	Model Compression Binary Networks	-
SI-VDNAS: Semi-Implicit Variational Dropout for Hierarchical One-shot Neural Architecture Search(Wang et al. 2020) accepted at IJCAI 2020	Search Strategy	-
An Empirical Study on the Robustness of NAS based Architectures(Devaguptapu et al. 2020)	Study	-
MergeNAS: Merge Operations into One for Differentiable Architecture Search(Wang et al. 2020) accepted at IJCAI 2020	Search Strategy	-
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search(Hong et al. 2020) accepted at IJCAI 2020	Search Strategy	-
Evolving Robust Neural Architectures to Defend from Adversarial Attacks(Kotyan and Vargas 2020) accepted at Proceedings of the Workshop on Artificial Intelligence Safety 2020	Adversarial Attacks and Defenses	Github
Architecture Search of Dynamic Cells for Semantic Video Segmentation(Nekrasov et al. 2020) accepted at WACV 2020	Video Models Semantic Segmentation	-
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search(Guo et al. 2020) accepted at ICML 2020	Search Strategy	-
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction(Song et al. 2020) accepted at KDD2020	CTR	-
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation(Yan et al. 2020)	Medical Image Segmentation	-
VINNAS: Variational Inference-based Neural Network Architecture Search(Ferianc et al. 2020)	-	-
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search(Peng et al. 2020) accepted at MICCAI 2020	Medical Multimodal Learning	-
Graph Neural Architecture Search(Gao et al. 2020) accepted at IJCAI 2020	GNN RL	Github
Ensembles of Networks Produced from Neural Architecture Search(Herron et al. 2020)	Neural Network Ensembles	-
Neural Architecture Search with GBDT(Luo et al. 2020)	Predictor-based	Github
A Study on Encodings for Neural Architecture Search(White et al. 2020)	Study Survey	Github
NASGEM: Neural Architecture Search via Graph Embedding Method(Cheng et al. 2020)	Estimation Strategy	-
An Evolution-based Approach for Efficient Differentiable Architecture Search(Kobayashi and Nagao) accepted at GECCO 2020	-	-
HyperFDA: a bi-level Optimization Approach to Neural Architecture Search and Hyperparameters’ optimization via fractal decomposition-based algorithm(Souquet et al. 2020) accepted at GECCO 2020	-	-
A Multi-objective architecture search for generative adversarial networks(Kobayashi et al. 2020) accepted at GECCO 2020	GAN	-
A first Step toward Incremental Evolution of Convolutional Neural Networks(Barnes et al. 2020) accepted at GECCO 2020	-	-
Computational model for neural architecture search(Gottapu 2020)	-	-
Neural Architecture Search for extreme multi-label classification: an evolutionary approach(Pauletto et al. 2020)	Multi-label Classification	-
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery(Cho et al. 2020)	-	-
Journey Towards Tiny Perceptual Super-Resolution(Lee et al. 2020)	Image Translator Super-Resolution	-
Self-supervised Neural Architecture Search(Kaplan and Giryes 2020)	-	-
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation(Wang et al. 2020)	Multi-objective Search	Github
Parametric machines: a fresh approach to architecture search(Vertechi et al. 2020)	-	-
Discretization-Aware Architecture Search(Tian et al. 2020)	-	-
GOLD-NAS: Gradual, One-Level, Differentiable(Bi et al. 2020)	-	-
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable(Wang et al. 2020)	Search Strategy	-
M-NAS: Meta Neural Architecture Search(Wang et al. 2020) accepted at AAAI 2020	Meta-learning	-
FiFTy: Large-scale File Fragment Type Identification using Convolutional Neural Networks(Mittal et al. 2020) accepted at IEEE Transactions on Information Forensics and Security	File-type Identification Forensics	-
RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks(Wang et al. 2020) accepted at IEEE Transactions on Geoscience and Remote Sensing	Remote Sensing	-
Theory-Inspired Path-Regularized Differential Network Architecture Search(Zhou et al. 2020)	Search Strategy	-
The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture(Li et al. 2020)	-	-
Semi-Discrete Optimization Through Semi-Discrete Optimal Transport: A Framework for Neural Architecture Search(Trillos and Morales 2020)	-	-
Traditional And Accelerated Gradient Descent for Neural Architecture Search(Trillos et al. 2020)	Search Strategy	-
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation(Kügler et al. 2020) accepted at MICCAI 2020	Pose Estimation	Github
Evolutionary Recurrent Neural Architecture Search(Tian et al. 2020) accepted at IEEE Embedded System Letters	Search Strategy	-
Neural-Architecture-Search-Based Multiobjective Cognitive Automation System(Wang et al. 2020) accepted at IEEE System Journal	Cognitive Computing Multi-objective Search	-
Enhancing Model Parallelism in Neural Architecture Search for Multi-device System(Fu et al. 2020) accepted at IEEE Micro	Multi-device System	-
AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction(Li et al. 2020) accepted at KDD 2020	Spatio-Temporal Prediction	-
Neural Architecture Search for Sparse DenseNets with Dynamic Compression(O’Neill et al. 2020) accepted at GECCO 2020	Search Strategy	-
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks(Zhou et al. 2020)	GAN	Github
Neural Architecture Design for GPU-Efficient Networks(Lin et al. 2020)	-	-
Equivalence in Deep Neural Networks via Conjugate Matrix Ensembles(Süzen 2020)	-	-
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL(Zimmer et al. 2020)	-	-
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search(Panda et al. 2020)	-	-
Tiny Video Networks: Architecture Search for Efficient Video Models(Piergiovanni et al. 2020) accepted at 7th ICML Workshop on Automated Machine Learning, 2020	Video Models	-
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR 2020	Semantic Segmentation Object Detection	Github
Neural networks adapting to datasets: learning network size and topology(Janik and Nowak 2020)	-	-
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning(Li et al. 2020)	Outlier Detection Imitation Learning	-
Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis(Yang et al. 2020) accepted at Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security	Image Steganalysis	-
Neural Architecture Search for Time Series Classification(Rakhshani et al. 2020) accepted at ijcnn 2020	Time Series	-
Cyclic Differentiable Architecture Search(Yu et al. 2020)	Search Strategy	Github
Differentially-private Federated Neural Architecture Search(Singh et al. 2020)	Federated Learning	Github
DrNAS: Dirichlet Neural Architecture Search(Chen et al. 2020)	Search Strategy	Github
Neural Architecture Optimization with Graph VAE(Li et al. 2020)	Estimation Strategy VAE GNN	-
Fine-Grained Stochastic Architecture Search(Chaudhuri et al. 2020)	Search Strategy	-
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners(Geada et al. 2020)	Search Strategy	Github
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks(Tian et al. 2020)	GAN DARTS	Github
Fine-Tuning DARTS for Image Classification(Tanveer et al. 2020)	DARTS	-
Neural Anisotropy Directions(Ortiz-Jiménez et al. 2020)	-	-
CryptoNAS: Private Inference on a ReLU Budget(Ghodsi et al. 2020)	Private Inference	-
Heuristic Architecture Search Using Network Morphism for Chest X-Ray Classification(Radiuk and Kutucu 2020)	Medical Network Morphism	-
Task-aware Performance Prediction for Efficient Architecture Search(Kokiopoulou et al. 2020) accepted at ECAI 2020	Estimation Strategy	-
Beyond Network Pruning: a Joint Search-and-Training Approach(Lu et al. 2020) accepted at IJCAI 2020	Multi-objective Search	-
Neural Ensemble Search for Performant and Calibrated Predictions(Zaidi et al. 2020)	Ensemble	-
Multi-fidelity Neural Architecture Search with Knowledge Distillation(Trofimov et al. 2020)	Estimation Strategy	-
Differentiable Neural Architecture Transformation for Reproducible Architecture Improvement(Kim et al. 2020)	-	-
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search(Nguyen et al. 2020)	Search Strategy	-
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel(Ru et al. 2020)	Search Strategy	-
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing(Klyuchnikov et al. 2020)	NLP Benchmark	-
Few-shot Neural Architecture Search(Zhao et al. 2020)	Estimation Strategy	-
NADS: Neural Architecture Distribution Search for Uncertainty Awareness(Ardywibowo et al. 2020)	-	-
Towards Efficient Automated Machine Learning(Li 2020)	Survey	-
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System(Zhao et al. 2020)	CTR	-
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges(Galvan and Mooney 2020)	Survey	-
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks(Fu et al. 2020) accepted at ICML 2020	GAN Image Translator	Github
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?(Yan et al. 2020)	Unsupervised Search Strategy	-
Hardware-Aware Transformable Architecture Search with Efficient Search Space(Jiang et al. 2020) accepted at accpeted at ICME 2020	Search Space Multi-objective Search	-
Sparse CNN Archtitecture Search(Yeshwanth et al. 2020) accepted at ICME 2020	-	-
Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics(Wei et al. 2020) accepted at ICME 2020	Image Forensics	-
Neural Architecture Search without Training(Mellor et al. 2020)	Estimation Strategy	Github
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search(Ru et al. 2020)	Estimation Strategy	-
Differentiable Neural Input Search for Recommender Systems(Cheng et al. 2020)	CTR	-
Efficient Architecture Search for Continual Learning(Gao et al. 2020)	Continual Learning	-
Conditional Neural Architecture Search(Kao et al. 2020)	Search Strategy GAN	-
AutoHAS: Differentiable Hyper-parameter and Architecture Search(Dong et al. 2020)	-	-
Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search(Qiang et al. 2020) accepted at Computerized Medical Imaging and Graphics	Medical Deep Belief Network	-
Fast Hardware-Aware Neural Architecture Search(Zhang et al. 2020) accepted at CVPR 2020 workshop	Multi-objective Search	-
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising(Zhang et al. 2020) accepted at CVPR 2020	Image Translator
GP-NAS: Gaussian Process based Neural Architecture Search(Li et al. 2020) accepted at CVPR 2020	Search Strategy	-
MemNAS: Memory-Efficient Neural Architecture Search with Grow-Trim Learning(Liu et al.2020) accepted at CVPR 2020	Multi-objective Search	Github
Can weight sharing outperform random architecture search? An investigation with TuNAS(Bender et al. 2020) accepted at CVPR 2020	Estimation Strategy	-
Butterfly Transform: An Efficient FFT Based Neural Architecture Design(Alizadeh vahid et al. 2020) accepted at CVPR 2020	Model Compression	Github
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy(Wang et al.2020) accepted at CVPR 2020	Multi-objective Search	Github
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection(Jiang et al. 2020) accepted at CVPR 2020	Object Detection	-
All in One Bad Weather Removal using Architectural Search(Li et al. 2020) accepted at CVPR 2020	Image Translator	-
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks(Lee and Lee) accepted at CVPR 2020	Model Compression	Github
On Network Design Spaces for Visual Recognition(Radosavovic et al. 2020) accepted at ICCV 2019	Image Recognition	Github
A Comprehensive Survey of Neural Architecture Search: Challanges and Solutions(Ren et al. 2020)	Survey	-
FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function(Dai et al. 2020)	-	-
Neural Architecture Search With Reinforce And Masked Attention Autoregressive Density Estimators(Krishna et al. 2020)	Search Strategy	-
Automation of Deep Learning – Theory and Practice(Wistuba et al. 2020) accepted at ICMR 2020	Survey	-
AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation(Baldeon Calisto and Lai-Yuen. 2020) accepted at Neural Networks	Medical Image Segmentation	-
DC-NAS: Divide-and-Conquer Neural Architecture Search(Wang et al. 2020)	Search Strategy	-
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens(Yang et al. 2020)	-	-
Designing Resource-Constrained Neural Networks Using Neural Architecture Search Targeting Embedded Devices(Cassimon et al. 2020) accepted at IEEE Internet of Things	Multi-objective Search	-
Searching Better Architectures for Neural Machine Translation(Fan et al. 2020) accepted at IEEE/ACM Transactions on Audio, Speech, and Language Processing	Language Modeling Machine Translation	-
Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations(Chen et al. 2020) accepted at IEEE Journal of Selected Topics in Signal Processing	Object Detection	-
A New Deep Neural Architecture Search Pipeline for Face Recognition(Zhu et al. 2020) accepted at IEEE Access	Face Recognition	-
Regularized Evolution for Marco Neural Architecture Search(Kyriakides and Margaritis) accepted at AIAI2020	Search Strategy	-
Evolutionary NAS with Gene Expression Programming of Cellular Encoding(Broni-Bediako et al. 2020)	-	-
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search(Rawal et al. 2020)	Search Strategy Estimation Strategy	Github
Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming(Suganuma et al. 2020) accepted at accepted in book on “Deep Neural Evolution”	Search Strategy	-
An Introduction to Neural Architecture Search for Convolutional Networks(Kyriakides and Margaritis, 2020)	Survey	-
AutoSegNet: An Automated Neural Network for Image Segmentation(Xu et al. 2020) accepted at IEEE Access	Medical Image Segmentation	-
DMS: Differentiable Dimension Search for Binary Neural Networks(Li et al. 2020) accepted at 1st Workshop on Neural Architecture Search at ICLR 2020	Search Strategy Binary Networks	-
Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects(Tsukada et al. 2020) accepted at accepted in book on “Deep Neural Evolution”	Medical	-
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy(Guo et al. 2020)	-	-
Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search(Dai et al. 2020) accepted at IEEE ISBI	Medical	-
Rethinking Performance Estimation in Neural Architecture Search(Zheng et al. 2020) accepted at CVPR 2020	Estimation Strategy	Github
Application of a genetic algorithm to search for the optimal convolutional neural network architecture with weight distribution(Radiuk 2020)	-	-
HNAS: Hierarchical Neural Architecture Search on Mobile Devices(Xia et al. 2020)	Search Strategy Model Compression	-
Improving Neuroevolution Using Island Extinction And Repopulation(Lyu et al. 2020)	Time Series Evolutionary	-
You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design(Chen et al. 2020)	Multi-objective Search	-
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation(Chen et al. 2020) accepted at INTERSPEECH 2020	Speech Recognition DARTS	-
A Semi-Supervised Assessor of Neural Architectures(Tang et al. 2020) accepted at CVPR 2020	Estimation Strategy GNN	-
Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging(Wang et al. 2020)	Medical	-
Binarizing MobileNet via Evolution-based Searching(Phan et al. 2020)	Model Compression	-
Neural Architecture Transfer(Lu et al. 2020)	Transfer Learning Evolutionary	Github
Optimization of deep neural networks: a survey and unified taxonomy(Talbi 2020)	Survey	-
Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing(Yu et al. 2020) accepted at accetped at ICASSP 2020	Face Anti-spoofing Model Compression	-
Neuro Evolutional with Game-Driven Cultural Algorithms(Waris and Reynolds 2020) accepted at ACM GECCO 2020	Game Playing Search Strategy	-
NASIL: Neural Architecture Search With Imitation Learning(Fard et al. 2020) accepted at ICASSP 2020	Imitation Learning Search Strategy	-
Noisy Differentiable Architecture Search(Chu et al. 2020)	Search Strategy	Github
AutoSpeech: Neural Architecture Search for Speaker Recognition(Ding et al. 2020)	Speech Recognition	Github
Learning Architectures from an Extended Search Space for Language Modeling(Li et al. 2020) accepted at ACL 2020	Language Modeling Search Space	-
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs( Zhuo et al. 2020)	Model Compression DARTS	-
Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches(Wang et al. 2020) accepted at accepted in book on “Deep Neural Evolution”	Search Strategy	-
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach(Alves and de Oliveira. 2020) accepted at IEEE CEC	Search Strategy	Github
Local Search is State of the Art for Neural Architecture Search Benchmarks(White et al. 2020) accepted at AutoML workshop at ICML’20	Search Strategy	Github
SIPA: A Simple Framework for Efficient Networks(Lee et al. 2020)	-	-
The effect of reduced training in neural architecture search(Kyriakides and Margaritis. 2020) accepted at Neural Comput & Applic	-	-
Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover(Tan et al. 2020) accepted at BIC-TA’20	Evolutionary	-
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators( Xiong et al. 2020)	Object Detection Model Compression	-
Angle-based Search Space Shrinking for Neural Architecture Search(Hu et al. 2020)	-	-
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching(Yu et al. 2020)	Medical	-
Deep Multimodal Neural Architecture Search(Yu et al. 2020)	Multimodal Learning	-
Depth-Wise Neural Architecture Search(Jordao et al. 2020)	-	-
Recurrent Neural Network Architecture Search for Geophyiscal Emulation(Maulik et al. 2020)	Emulators Simulation Evolutionary	-
Local Search is a Remarkably Strong Baseline for Neural Architecture Search(Ottelander et al. 2020)	-	-
Superkernel Neural Architecture Search for Image Denoising(Mozejko et al. 2020) accepted at NTIRE2020 Workshop at CVPR 2020	Image Denoising Unsupervised	-
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search(Guo et al. 2020)	Medical DARTS	-
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks(Chen et al. 2020)	-	-
A Neural Architecture Search based Framework for Liquid State Machine Design(Tian et al. 2020)	-	-
Geometry-Aware Gradient Algorithms for Neural Architecture Search(Li et al. 2020)	-	-
Distributed Evolution of Deep Autoencoders(Hajewski et al. 2020)	Distributed System Evolutionary	-
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions(Wan et al. 2020)	-	-
ModuleNet: Knowledge-inherited Neural Architecture Search(Chen et al. 2020)	-	-
Evolutionary recurrent neural network for image captioning(Wang et al. 2020) accepted at Neurocomputing	Image Captioning Cross-modal Evolutionary	-
Neural Architecture Search for Lightweight Non-Local Networks(Li et al. 2020)	-	-
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS(Ning et al. 2020)	GNN Predictor-based	-
FedNAS: Federated Deep Learning via Neural Architecture Search(He et al. 2020) accepted at CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning	Federated Learning	Github
An Evolutionary Approach to Variational Autoencoders(Hajewski and Oliveira. 2020) accepted at CCWC’20	Variational Autoencoder Unsupervised Evolutionary	-
A Scalable System for Neural Architecture Search(Hajewski and Oliveira. 2020) accepted at CCWC’20	Distributed System	-
Neural Architecture Generator Optimization(Ru et al. 2020)	-	-
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning(Dey et al. 2020)	Bayesian Optimization	Github
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning(Gao et al. 2020) accepted at CVPR’20	Multi-Task Learning One-shot Gradient-based	Github
Designing Network Design Spaces(Radosavovic et al. 2020) accepted at CVPR’20	-	-
Disturbance-immune Weight Sharing for Neural Architecture Search(Niu et al. 2020)	-	-
NPENAS:Neural Predictor Guided Evolution for Neural Architecture Search(Wei et al. 2020)	-	-
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search(Dai et al. 2020)	-	-
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation(He et al. 2020) accepted at CVPR’20	MiLeNAS	Github
Are Labels Necessary for Neural Architecture Search?(Liu et al. 2020)	Unsupervised DARTS	Github
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation(Zhang et al. 2020)	Image Segmentation	-
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection(Guo et al. 2020) accepted at CVPR 2020	Object Detection DARTS	Github
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search(Zhang et al. 2020)	Evolutionary	-
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet(You et al. 2020) accepted at CVPR’2020	GreedyNAS	-
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models(Yu et al. 2020)	-	-
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting(Wu et al. 2020)	-	-
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels(Shen et al. 2020)	-	-
Probabilistic Dual Network Architecture Search on Graphs(Zhao et al. 2020)	GNN Gradient-based	-
GAN Compression: Efficient Architectures for Interactive Conditional GAN(Li et al. 2020)	GAN Model Compression One-shot	Github
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection(Jiang et al. 2020)	Medical Lesion Detection	-
Lifelong Learning with Searchable Extension Units(Wang et al. 2020)	Lifelong Learning	-
Efficient Backbone Search for Scene Text Recognition(Zhang et al. 2020)	Scene Text Recognition	-
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Erickson et al. 2020)	Structured Data	-
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment(Huang and Chu. 2020)	-	-
Hierarchical Neural Architecture Search for Single Image Super-Resolution(Guo et al. 2020)	Image Translator	-
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS(Yu et al. 2020)	-	-
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch(Real et al. 2020) accepted at ICML 2020	AutoML	Github
Accelerator-Aware Neural Network Design Using AutoML(Gupta and Akin. 2020) accepted at On-device Intelligence Workshop at MLSys’20	Model Compression	-
Real-time Federated Evolutionary Neural Architecture Search(Zhu and Jin. 2020)	Federated Learning Evolutionary	-
BATS: Binary ArchitecTure Search(Bulat et al. 2020) accepted at ECCV’20	Binary Networks DARTS	Github
ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search(Zhang et al. 2020)	-	-
NAS-Count: Counting-by-Density with Neural Architecture Search(Hu et al. 2020)	-	-
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures(Kefan and Pang. 2020)	-	-
Neural Inheritance Relation Guided One-Shot Layer Assignment Search(Meng et al. 2020)	-	-
Automatically Searching for U-Net Image Translator Architecture(Shu and Wang. 2020)	Image Translator Evolutionary	-
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations(Zhao et al. 2020)	CTR DARTS	-
Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search(Hong et al. 2020) accepted at WACV’20 workshop	Scene Text Recognition Image Recognition ProxylessNAS	-
Search for Winograd-Aware Quantized Networks(Fernandez-Marques et al. 2020)	Model Compression Winograd ProxylessNAS	-
Semi-Supervised Neural Architecture Search(Luo et al. 2020)	SemiNAS NAO	Github
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction(Yan et al. 2020)	Medical Magnetic Resonance Imaging DARTS	Github
DSNAS: Direct Neural Architecture Search without Parameter Retraining(Hu et al. 2020)	DSNAS	Github
Neural Architecture Search For Fault Diagnosis(Li et al. 2020) accepted at ESREL’20	Fault Diagnosis RL Controller-based	-
Learning Architectures for Binary Networks(Kim et al. 2020) accepted at ECCV’20	Binary Networks DARTS	-
Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB(Johner and Wassner. 2020) accepted at ICMLA’19	Model Compression Evolutionary	-
Automating Deep Neural Network Model Selection for Edge Inference(Lu et al. 2020) accepted at CogMI’20	Model Compression	-
Neural Architecture Search over Decentralized Data(Xu et al. 2020)	Federated Learning	-
Automatic Structural Search for Multi-task Learning VALPs(Garciarena et al. 2020) accepted at OLA’20	Multi-task Learning	-
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning(Alletto et al. 2020) accepted at Meta-Eval 2020 workshop	Multimodal Learning	-
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization(Chen and Hsieh. 2020)	DARTS	-
Best of Both Worlds: AutoML Codesign of a CNN and its Model Compression(Abdelfattah et al. 2020) accepted at DAC’20	Model Compression RL	-
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks(Yang et al. 2020) accepted at DAC’20	Model Compression ASIC RL Controller-based	-
FPNet: Customized Convolutional Neural Network for FPGA Platforms(Yang et al. 2020) accepted at FPT’20	Model Compression FPGA RL Controller-based	-
AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning(Basha et al. 2020)	Transfer Learning CV Bayesian Optimization	-
NASS: Optimizing Secure Inference via Neural Architecture Search(Bian et al. 2020) accepted at ECAI’20	Secure Inference Privacy Controller-based	-
Search for Better Students to Learn Distilled Knowledge(Gu et al. 2020) accepted at ECAI'20	Model Compression Knowledge Distillation DARTS	-
Bayesian Neural Architecture Search using A Training-Free Performance Metric(Camero et al. 2020)	RNN Bayesian Optimization	-
NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search(Zela et al. 2020) accepted at ICLR’20	Benchmark One-shot	-
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification(Chen et al. 2010)	Remote Sensing RL	-
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient(Chen et al. 2020)	RL Controller-based	-
Efficient Neural Architecture Search: A Broad Version(Ding et al. 2020)	ENAS	-
ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel(Fan et al. 2020)	Medical Image Segmentation Evolutionary	-
FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks(Iqbal et al. 2020)	Multi-objective Search Bayesian Optimization	Github
Up to two billion times acceleration of scientific simulations with deep neural architecture search(Kasim et al. 2020)	Scientific Simulations ProxylessNAS	-
Latency-Aware Differentiable Neural Architecture Search(Xu et al. 2020)	DARTS	-
MixPath: A Unified Approach for One-shot Neural Architecture Search(Chu et al. 2020)	One-shot	-
Neural Architecture Search for Skin Lesion Classification(Kwasigroch et al. 2020) accepted at IEEE Access	Medical Image Classification Network Morphism	-
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search(Chen et al. 2020)	BERT Model Compression NLP DARTS	-
Neural Architecture Search for Deep Image Prior(Ho et al. 2020)	Image Denoising Image Inpainting Image Super-resolution Evolutionary	-
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(Fang et al. 2020) accepted at ICLR’20	Object Detection Image Segmentation DARTS	Github
FTT-NAS: Discovering Fault-Tolerant Neural Architecture(Li et al. 2020) accepted at ASP-DAC 2020	Multi-objective Search RL	-
Deeper Insights into Weight Sharing in Neural Architecture Search(Zhang et al. 2020)	Survey Weight Sharing One-shot	-
EcoNAS: Finding Proxies for Economical Neural Architecture Search(Zhou et al. 2020) accepted at CVPR’20	Evolutionary	-
DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems(Loni et al. 2020) accepted at Microprocessors and Microsystems	Multi-objective Search Evolutionary	-
Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification(Ma et al. 2020) accepted at IEEE Access	3D Deep learning DARTS	-
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(Dong and Yang et al. 2020) accepted at ICLR’20	Benchmark	Github

2019

Title	Tags	Code
Scalable NAS with Factorizable Architectural Parameters(Wang et al. 2019)	-	-
Modeling Neural Architecture Search Methods for Deep Networks(Malekhosseini et al. 2019)	-	-
Searching for Stage-wise Neural Graphs in the Limit(Zhou et al. 2019)	-	-
Neural Architecture Search on Acoustic Scene Classification(Li et al. 2019)	-	-
RC-DARTS: Resource Constrained Differentiable Architecture Search(Jin et al. 2019)	-	-
NAS Evaluation is frustatingly hard(Yang et al. 2019) accepted at ICLR’20	-	-
A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network(Singh et al. 2019)	-	-
BetaNAS: Balanced Training and Selective Drop for Neural Architecture Search(Fang et al. 2019)	-	-
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild(Chen et al. 2019)	-	-
TextNAS: A Neural Architecture Search Space tailored for Text Representation(Wang et al. 2019)	-	-
AtomNAS: Fine-Grined End-To-End Neural Architecture Search(Mei et al. 2019) accepted at ICLR’20	-	-
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation(Yu et al. 2019)	Medical Image Segmentation	-
A Reinforcement Neural Architecture Search Method for Rolling Bearing Fault Diagnosis(Wang et al. 2019) accepted at Measurement	-	-
Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data(Quiang et al. 2019) accepted at MMMI’19	-	-
QoS-aware Neural Architecture Search(Cheng et al. 2019) accepted at NeurIPS’19	-	-
Neural-Hardware Architecture Search(Lin et al. 2019) accepted at NeurIPS’19	-	-
Preventing Information Leakage with Neural Architecture Search(Zhang et al. 2019)	-	-
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data(Such et al. 2019)	-	-
UNAS: Differentiable Architecture Search Meets Reinforcement Learning(Vahdat et al. 2019)	-	-
Efficient network architecture search via multiobjective particle swarm optimization based on decomposition(Jiang et al. 2019)	-	-
Deep Uncertainty Estimation for Model-based Neural Architecture Search(White et al. 2019) accepted at workshop on Bayesian Deep Learning at NeurIPS’19	-	-
A Variational-Sequential Graph Autoencoder for Neural Architecture Performance Prediction(Friede et al. 2019)	-	-
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods(Hassantabar et al. 2019)	-	-
Leveraging End-to-End Speech Recognition with Neural Architecture Search(Baruwa et al. 2019)	-	-
Efficient Differentiable Neural Architecture Search with Meta Kernels(Chen et al. 2019)	-	-
Neural architecture search for image saliency fusion(Bianco et al. 2019) accepted at Information Fusion	-	-
Ultrafast Photorealistic Style Transfer via Neural Architecture Search(An et al. 2019)	-	-
AdversarialNAS: Adversarial Neural Architecture Search for GANs(Gao et al. 2019)	-	-
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification(Doveh et al. 2019)	-	-
SGAS: Sequential Greedy Architecture Search(Li et al. 2019) accepted at CVPR’20	-	-
Blockwisely Supervised Neural Architecture Search with Knowledge Distillation(Li et al. 2019)	-	-
Towards Oracle Knowledge Distillation with Neural Architecture Search(Kang et al. 2019)	-	-
AutoML for Architecting Efficient and Specialized Neural Networks(Cai et al. 2019) accepted at IEEE Micro	-	-
Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms(Colangelo et al. 2019) accepted at HPEC’19	-	-
Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification(Chen et al. 2019) accepted at SMC’19	-	-
Performance Prediction Based on Neural Architecture Features(Long et al. 2019) accepted at CCHI’19	-	-
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search(Chu et al. 2019) accepted at ECCV’20	-	-
EDAS: Efficient and Differentiable Architecture Search(Hong et al. 2019)	-	-
SGAS: Sequential Greedy Architecture Search(Li et al. 2019)	-	-
Ranking architectures using meta-learning(Dubatovka et al. 2019)	-	-
Meta-Learning of Neural Architectures for Few-Shot Learning(Elsken et al. 2019)	-	-
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks(Guo et al. 2019)	-	-
Exploiting Operation Importance for Differentiable Neural Architecture Search(Xie et al. 2019)	-	-
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection(Yao et al. 2019)	-	-
Multi-Objective Neural Architecture Search via Predictive Network Performance Optimization(Shi et al. 2019)	-	-
Data Proxy Generation for Fast and Efficient Neural Architecture Search(Park. 2019)	-	-
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture(Zhang et al. 2019)	-	-
Search to Distill: Pearls are Everywhere but not the Eyes(Liu et al. 2019)	-	-
EfficientDet: Scalable and Efficient Object Detection(EfficientDet: Scalable and Efficient Object Detection)	-	-
Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search(Süzen et al. 2019)	-	-
IMMUNECS: Neural Committee Search by an Artificial Immune System(IMMUNECS: Neural Committee Search by an Artificial Immune System)	-	-
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving(Hao et al. 2019)	-	-
Neural Recurrent Structure Search for Knowledge Graph Embedding(Zhang et al. 2019)	-	-
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search(Yuan et al. 2019)	-	-
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification(Dong et al. 2019)	-	-
Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover(Baughmann and Wozniak. 2019) accepted at Supercomputing’19	-	-
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search(Green et al. 2019)	-	-
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters(Xiao et al. 2019) accepted at NeurIPS’19	-	-
DATA: Differentiable ArchiTecture Approximation(Chang et al. 2019) accepted at NeurIPS’19	-	-
Learning to reinforcement learn for Neural Architecture Search(Robles and Vanschoren. 2019)	-	-
An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification(Sharaf ad Radwan. 2019) accepted at accepted book chapter	-	-
ENAS Oriented Layer Adaptive Data Scheduling Strategy for Resource Limited Hardware(Li et al. 2019) accepted at Neurocomputing Journal	-	-
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition(Jiang et al. 2019) accepted at EMNLP-IJCNLP’19	-	-
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators(Jiang et al. 2019)	-	-
On Neural Architecture Search for Resource-Constrained Hardware Platforms(Lu et al. 2020) accepted at ICCAD’19	-	-
NAT: Neural Architecture Transformer for Accurate and Compact Architectures(Guo et al. 2019)	-	-
Deep neural network architecture search using network morphism(Kwasigroch et al. 2019) accepted at accepted MMAR’19	-	-
Person Re-identification with Neural Architecture Search(Zhang et al. 2019) accepted at accepted PRCV’19	-	-
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?(Xiong et al. 2019) accepted at ICCV’19	-	-
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification(Xu et al. 2019) accepted at ICCV’19	Object Detection	-
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search(White et al. 2019)	-	-
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters(Bi et al. 2019)	-	-
An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models(Klaiber et al. 2019)	-	-
Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework(Hardware-aware one-short Neural Architecture Search in Coordinate Ascent Framework)	-	-
Efficient Structured Pruning and Architecture Searching for Group Convolution(Zhao and Luk. 2019) accepted at ICCV’19 workshop	-	-
On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-tuning(Cai et al. 2019) accepted at ICCV’19 workshop	-	-
MSNet: Structural Wired Neural Architecture Search for Internet of Things(Cheng et al. 2019) accepted at ICCV’19 workshop	-	-
Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling(Lee et al. 2019)	-	-
Using Neural Architecture Search to Optimize Neural Networks for Embedded Devices(Cassimon et al. 2019) accepted at 3PGCIC’19	-	-
NASIB: Neural Architecture Search withIn Budget(Singh et al. 2019)	-	-
State of Compact Architecture Search For Deep Neural Networks(Shafiee et al. 2019)	-	-
One-Shot Neural Architecture Search via Self-Evaluated Template Network(Dong and Yang. 2019)	-	-
Scalable Neural Architecture Search for 3D Medical Image Segmentation(Kim et al. 2019) accepted at MICCAI’19	Medical Image Segmentation	-
Neural Architecture Search for Adversarial Medical Image Segmentation(Dong et al. 2019) accepted at MICCAI’19	Medical Image Segmentation	-
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation(Yang et al. 2019) accepted at MICCAI’19	Medical Image Segmentation	-
Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net(Zhang et al. 2019) accepted at MICCAI’19	-	-
Energy-aware Neural Architecture Optimization with Fast Splitting Steepest Descent(Wang et al. 2019) accepted at accepted EMC2 workshop’19	-	-
Improving one-shot NAS by Surppressing the Posterior Fading(Li et al. 2019)	-	-
Splitting Steepest Descent for Growing Neural Architectures(Liu et al. 2019)	-	-
A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm(Ahmed et al. 2019) accepted at AISI’19	-	-
RNAS: Architecture Ranking for Powerful Networks(Xu et al. 2019)	-	-
Towards Unifying Neural Architecture Space Exploration and Generalization(Bhardwaj and Marculescu)	-	-
Sub-Architecture Ensemble Pruning in Neural Architecture Search(Bia et al. 2019)	-	-
Towards modular and programmable architecture search(Negrinho et al. 2019) accepted at NeurIPS’19	-	-
Automated design of error-resilient and hardware-efficient deep neural networks(Schorn et al. 2019)	-	-
STACNAS: Towards Stable and Consistent Optimization for Differentiable Neural Architecture Search(Guilin et al. 2019)	-	-
Efficient Residual Dense Block Search for Image Super-Resolution(Song et al. 2019)	-	-
Understanding and Improving One-shot Neural Architecture Optimization(Luo et al. 2019)	-	-
Scheduled Differentiable Architecture Search for Visual Recognition(Qui et al. 2019)	-	-
Understanding and Robustifying Differentiable Architecture Search(Zela et al. 2019) accepted at ICLR’20	-	-
Genetic Neural Architecture Search for automatic assessment of human sperm images(Miahi et al. 2019)	-	-
IR-NAS: Neural Architecture Search for Image Restoration(Zhang et al. 2019)	-	-
Pose Neural Fabrics Search(Yang et al. 2019)	-	-
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation(Wong and Moradi. 2019)	3D Medical Image Segmentation	-
DARTS+: Improved Differentiable Architecture Search with Early Stopping(Liang et al. 2019)	-	-
Searching for Accurate Binary Neural Architectures(Shen et al. 2019) accepted at ICCV’19 Neural Architects workshop	-	-
Improving Keyword Spotting and Language Identification via Neural Architecture Search at Scale(Mazzawi et al. 2019) accepted at INTERSPEECH 2019	-	-
Neural Architecture Search for Class-incremental Learning(Huang et al. 2019)	-	-
Graph-guided Architecture Search for Real-time Semantic Segmentation(Lin et al. 2019)	Image Segmentation	-
CARS: Continuous Evolution for Efficient Neural Architecture Search(Yang et al. 2019) accepted at CVPR’20	-	-
Bayesian Optimization of Neural Architectures for Human Activity Recognition(Osmani and Hamidi. 2019) accepted at Human Activity Sensing	-	-
Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm(Litzinger et al. 2019) accepted at ICANN’19	-	-
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study(Faes et al. 2019) accepted at The Lancet Digital Health	-	-
A greedy constructive algorithm for the optimization of neural network architectures(Pasini et al. 2019)	-	-
Differentiable Mask Pruning for Neural Networks(Ramakrishnan et al. 2019)	-	-
Neural Architecture Search in Embedding Space(Liu. 2019)	-	-
Auto-GNN: Neural Architecture Search of Graph Neural Networks(Zhou et al. 2019)	-	-
Best Practices for Scientific Research on Neural Architecture Search(Lindauer and Hutter. 2019)	-	-
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection(Peng et al. 2019)	-	-
Training compact neural networks via auxiliary overparameterization(Liu et al. 2019)	-	-
Rethinking the Number of Channels for Convolutional Neural Networks(Zhu et al. 2019)	-	-
MANAS: Multi-Agent Neural Architecture Search(Carlucci et al. 2019)	-	-
Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation(Bae et al. 2019) accepted at MICCAI’19	Medical Image Segmentation	-
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge(Zhang et al. 2019)	-	-
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research(Balaprakash et al. 2019) accepted at SC’19	-	-
Automatic Neural Network Search Method for Open Set Recognition(Sun et al. 2019) accepted at ICIP’19	-	-
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking(Yan et al. 2019) accepted at ICCV’19 Neural Architects Workshop	-	-
Once for All: Train One Network and Specialize it for Efficient Deployment(Cai et al. 2019)	-	-
Refactoring Neural Networks for Verification(Shriver et al. 2019)	-	-
CNASV: A Convolutional Neural Architecture Search-Train Prototype for Computer Vision Task(Zhou and Yang. 2019) accepted at CollaborateCom’19	-	-
Automatic Design of Deep Networks with Neural Blocks(Zhong et al. 2019) accepted at Cognitive Computation	-	-
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks(Zhang et al. 2019)	-	-
SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search(Chu et al. 2019)	-	-
A Novel Encoding Scheme for Complex Neural Architecture Search(Ahmad et al. 2019) accepted at ITC-CSCC	-	-
A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design(Irwin-Harris et al. 2019) accepted at accepted CEC’19	-	-
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain(Verma et al. 2019) accepted at AIKE’19	-	-
Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms(Tian et al. 2019)	-	-
AutoGAN: Neural Architecture Search for Generative Adversarial Networks(Gong et al. 2019) accepted at ICCV’19	GAN	Github
Refining the Structure of Neural Networks Using Matrix Conditioning(Yousefzadeh and O’Leary. 2019)	-	-
SqueezeNAS: Fast neural architecture search for faster semantic segmentation(Shaw et al. 2019)	Image Segmentation	-
MoGA: Searching Beyond MobileNetV3(Chu et al. 2019) accepted at ICASSP’20	-	-
Evolving deep neural networks by multi-objective particle swarm optimization for image classification(Wang et al. 2019) accepted at GECCO’19	-	-
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks(Wang et al. 2019) accepted at IEEE CEC’20	-	-
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation(Calisto and Lai-Yuen. 2019) accepted at SPIE Medical Imaging’20	Medical Image Segmentation	-
MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning(by Liu et al. 2019)	-	-
Efficient Novelty-Driven Neural Architecture Search(Zhang et al. 2019)	-	-
PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search(Xu et al. 2019)	-	-
Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019)	-	-
EPNAS: Efficient Progressive Neural Architecture Search(Zhou et al. 2019)	-	-
Video Action Recognition via Neural Architecture Searching(Peng et al. 2019)	Video Models	-
Hardware/Software Co-Exploration of Neural Architectures(Jiang et al. 2019) accepted at ASP-DAC’20	-	-
When Neural Architecture Search Meets Hardware Implementation: from Hardware Awareness to Co-Design(Zhang et al. 2019) accepted at ISVLSI’19	-	-
Reinforcement Learning for Neural Architecture Search: A Review(Jaafra et al. 2019 accepted at Image and Vision Computing)	-	-
Architecture Search for Image Inpainting(Li and King. 2019. accepted at International Symposium on Neural Networks)	-	-
Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression(Märtens and Izzo. 2019)	-	-
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search(Chu et al. 2019)	-	-
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search(Lakhmiri et al. 2019)	-	-
Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-based Performance Predictor(Sun et al. 2019) accepted at accepted by IEEE Transactions on Evolutionary Computation	-	-
Adaptive Genomic Evolution of Neural Network Topologies(Behjat et al. 2019) accepted at accepted and presented in ICRA 2019	-	-
Densely Connected Search Space for More Flexible Neural Architecture Search(Fang et al. 2019)	-	-
Posterior-Guided Neural Architecture Search(Zhou et al. 2020) accepted at AAAI	-	-
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019)	-	-
Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents(Borsos et al. 2019)	-	-
XNAS: Neural Architecture Search with Expert Advice(Nayman et al. 2019) accepted at NeurIPS’19	-	-
A Study of the Learning Progress in Neural Architecture Search Techniques(Singh et al. 2019)	-	-
Hardware aware Neural Network Architectures(Srinivas et al. 2019)	-	-
Sample-Efficient Neural Architecture Search by Learning Action Space(Wang et al. 2019)	-	-
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures(Cheng et al. 2019)	-	-
Automatic Modulation Recognition Using Neural Architecture Search(Wei et al. 2019) accepted at accepted High Performance Big Data and Intelligent Systems	-	-
Continual and Multi-Task Architecture Search(Pasunuru and Bansal. 2019)	-	-
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks(Wen et al. 2019)	-	-
One-Short Neural Architecture Search via Compressing Sensing(Cho et al. 2019)	-	-
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation(Zhu et al. 2019)	Medical Image Segmentation	-
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks(An et al. 2019)	-	-
Efficient Forward Architecture Search(Hu et al. 2019) accepted at NeurIPS’19	-	-
Differentiable Neural Architecture Search via Proximal Iterations(Yao et al. 2019)	-	-
Dynamic Distribution Pruning for Efficient Network Architecture Search(Zheng et al. 2019)	-	-
Particle swarm optimization of deep neural networks architectures for image classification(Fernandes Junior and Yen. 2019. accepted at Swarm and Evolutionary Computation)	-	-
On Network Design Spaces for Visual Recognition(Radosavovic et al. 2019) accepted at ICCV’20	-	-
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures(Ryoo et al. 2019)	Video Models	-
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks(Tan and Le) accepted at ICML’19. 2019	-	-
Structure Learning for Neural Module Networks(Pahuja et al. 2019)	-	-
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers(Fedorov et al. 2019) accepted at NeurIPS’19	-	-
Network Pruning via Transformable Architecture Search(Dong and Yang. 2019) accepted at NeurIPS’19	-	-
DEEP-BO for Hyperparameter Optimization of Deep Networks(Cho et al. 2019)	-	-
Constrained Design of Deep Iris Networks(Nguyen et al. 2019)	-	-
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search(Akimoto et al. 2019) accepted at ICML’19	-	-
Multinomial Distribution Learning for Effective Neural Architecture Search(Zheng et al. 2019)	-	-
EENA: Efficient Evolution of Neural Architecture(Zhu et al. 2019) accepted at ICCV’19 Neural Architects Workshop	-	-
DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence(Byla and Pang. 2019)	-	-
AutoDispNet: Improving Disparity Estimation with AutoML(Saikia et al. 2019)	-	-
Online Hyper-parameter Learning for Auto-Augmentation Strategy(Lin et al. 2019)	-	-
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search(Saltori et al. 2019)	-	-
Deep Neural Architecture Search with Deep Graph Bayesian Optimization(Ma et al. 2019)	-	-
Automatic Model Selection for Neural Networks(Laredo et al. 2019)	-	-
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization(Klein and Hutter. 2019)	-	-
BayesNAS: A Bayesian Approach for Neural Architecture Search(Zhou et al. 2019) accepted at ICML’19	-	-
Single-Path NAS: Device-Aware Efficient ConvNet Design(Stamoulis et al. 2019)	-	-
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection(Assuncao et al. 2019)	-	-
Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS(Jiang et al. 2019)	-	-
Fast and Reliable Architecture Selection for Convolutional Neural Networks(Hahn et al. 2019)	-	-
Differentiable Architecture Search with Ensemble Gumbel-Softmax(Chang et al. 2019)	-	-
Searching for A Robust Neural Architecture in Four GPU Hours(Dong and Yang 2019) accepted at CVPR’19	-	-
Evolving unsupervised deep neural networks for learning meaningful representations(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)	-	-
Evolving Deep Convolutional Neural Networks for Image Classification(Sun et al. 2019, accepted by IEEE Transactions on Evolutionary Computation)	-	-
AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation(Baldeon-Calisto and Lai-Yuen. 2019.) accepted at Neurocomputing	Medical Image Segmentation	-
Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification(Chen et al. 2019) accepted at IEEE Transactions on Geoscience and Remote Sensing	-	-
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation(Chen et al. 2019)	-	-
Design Automation for Efficient Deep Learning Computing(Han et al. 2019)	-	-
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification(Pakrashi and Namee 2019)	-	-
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning(Gao et al. 2019)	-	-
Neural Architecture Search for Deep Face Recognition(Zhu. 2019)	-	-
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation(Gessert and Schlaefer. 2019)	Medical Image Segmentation	-
NAS-Unet: Neural Architecture Search for Medical Image Segmentation(Weng et al. 2019) accepted at IEEE Access	Medical Image Segmentation	-
Fast DENSER: Efficient Deep NeuroEvolution(Assunção et al. 2019) accepted at ECGP’19	-	-
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection(Ghaisi et al. 2019) accepted at CVPR’19	-	-
Automated Search for Configurations of Deep Neural Network Architectures(Ghamizi et al. 2019) accepted at SPLC’19	-	-
WeNet: Weighted Networks for Recurrent Network Architecture Search(Huang and Xiang. 2019)	-	-
Resource Constrained Neural Network Architecture Search(Xiong et al. 2019)	-	-
Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach(Cetto et al. 2019) accepted at INNSBDDL	-	-
ASAP: Architecture Search, Anneal and Prune(Noy et al. 2019)	-	-
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours(Stamoulis et al. 2019)	-	-
Template-Based Automatic Search of Compact Semantic Segmentation Architectures(Nekrasov et al. 2019)	Image Segmentation	-
Exploring Randomly Wired Neural Networks for Image Recognition(Xie et al. 2019)	-	-
Understanding Neural Architecture Search Techniques(Adam and Lorraine 2019)	-	-
Automatic Convolutional Neural Architecture Search for Image Classification Under Different Scenes(Weng et al. 2019) accepted at accepted for IEEE Access	-	-
Single Path One-Shot Neural Architecture Search with Uniform Sampling(Guo et al. 2019)	-	-
Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers(Yu and Huang 2019)	-	-
sharpDARTS: Faster and More Accurate Differentiable Architecture Search(Hundt et al. 2019)	-	-
DetNAS: Neural Architecture Search on Object Detection(Chen et al. 2019) accepted at NeurIPS’19	Object Detection	megvii-code
Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming(Suganuma et al. 2019) accepted at Evolutionary Computation	-	-
Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising(Liu et al. 2019) accepted at Medical Image Analysis	-	-
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly(Kandasamy et al. 2019)	-	-
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design(Wong et al. 2019)	-	-
Improving Neural Architecture Search Image Classifiers via Ensemble Learning(Macko et al. 2019)	-	-
Software-Defined Design Space Exploration for an Efficient AI Accelerator Architecture(Yu et al. 2019)	-	-
MFAS: Multimodal Fusion Architecture Search(Pérez-Rúa et al. 2019) accepted at CVPR’19	Multimodal Learning	-
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks(Wang et al. 2019) accepted at PRICAI’19	-	-
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search(Li et al. 2019)	-	-
Inductive Transfer for Neural Architecture Optimization(Wistuba and Pedapati 2019)	-	-
Evolutionary Cell Aided Design for Neural Network(Colangelo et al. 2019)	-	-
Automated Architecture-Modeling for Convolutional Neural Networks(Duong 2019)	-	-
Learning Implicitly Recurrent CNNs Through Parameter Sharing(Savarese and Maire) accepted at ICLR’19	-	-
Evaluating the Search Phase of Neural Architecture Searc(Sciuto et al. 2019)	-	-
Random Search and Reproducibility for Neural Architecture Search(Li and Talwalkar 2019)	-	-
Evolutionary Neural AutoML for Deep Learning(Liang et al. 2019)	-	-
Fast Task-Aware Architecture Inference(Kokiopoulou et al. 2019)	-	-
Probabilistic Neural Architecture Search(Casale et al. 2019)	-	-
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution(Ororbia et al. 2019)	-	-
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search(Jiang et al. 2019) accepted at DAC’19	-	-
The Evolved Transformer(So et al. 2019)	-	-
Designing neural networks through neuroevolution(Stanley et al. 2019) accepted at Nature Machine Intelligence	-	-
NeuNetS: An Automated Synthesis Engine for Neural Network Design(Sood et al. 2019)	-	-
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search(Chu et al. 2019) accepted at ICPR’20	-	-
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search(Fang et al. 2019)	-	-
Bayesian Learning of Neural Network Architectures(Dikov et al. 2019)	-	-
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation(Liu et al. 2019) accepted at CVPR’19	Image Segmentation	Github
The Art of Getting Deep Neural Networks in Shape(Mammadli et al. 2019) accepted at TACO Journal	-	-
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search(Chu et al. 2019)	-	-

2018

Title	Tags	Code
A particle swarm optimization-based flexible convolutional auto-encoder for image classification(Sun et al. 2018, published by IEEE Transactions on Neural Networks and Learning Systems)	-	-
SNAS: Stochastic Neural Architecture Search(Xie et al. 2018) accepted at ICLR’19	SNAS	Github
Graph Hypernetworks for Neural Architecture Search(Zhang et al. 2018) accepted at Accepted at ICLR’19	-	-
Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution(Elsken et al. 2018) accepted at ICLR’19	-	-
Macro Neural Architecture Search Revisited(Hu et al. 2018) accepted at Meta-Learn NeurIPS workshop’18	-	-
AMLA: an AutoML frAmework for Neural Network Design(Kamath et al. 2018) accepted at at ICML AutoML workshop	-	-
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation(Dai et al. 2018)	-	-
Neural Architecture Search Over a Graph Search Space(de Laroussilhe et al. 2018)	-	-
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search(Jaafra et al. 2018)	-	-
Evolutionary Neural Architecture Search for Image Restoration(van Wyk and Bosman 2018)	-	-
IRLAS: Inverse Reinforcement Learning for Architecture Search(Guo et al. 2018) accepted at CVPR’19	-	-
FBNet: Hardware-Aware Efficient ConvNet Designvia Differentiable Neural Architecture Search(Wu et al. 2018) accepted at CVPR’19	-	-
ShuffleNASNets: Efficient CNN models throughmodified Efficient Neural Architecture Search(Laube et al. 2018)	-	-
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(Cai et al. 2018) accepted at ICLR’19	-	-
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search(Wu et al. 2018)	-	-
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification(Wang et al. 2018) accepted at CEC’18	-	-
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification(Wang et al. 2018) accepted at accepted AI’18	-	-
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks(Cai et al. 2018)	-	-
Evolving Space-Time Neural Architectures for Videos(Piergiovanni et al. 2018) accepted at ICCV’19	Video Models	-
InstaNAS: Instance-aware Neural Architecture Search(Cheng et al. 2018)	-	-
Evolutionary-Neural Hybrid Agents for Architecture Search(Maziarz et al. 2018) accepted at ICML’19 workshop on AutoML	-	-
Joint Neural Architecture Search and Quantization(Chen et al. 2018)	-	-
Transfer Learning with Neural AutoML(Wong et al. 2018) accepted at NeurIPS’18	-	-
Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation(Fielding and Zhang 2018)	-	-
Deep Active Learning with a Neural Architecture Search(Geifman and El-Yaniv 2018) accepted at NeurIPS’19	-	-
Stochastic Adaptive Neural Architecture Search for Keyword Spotting(Véniat et al. 2018)	-	-
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search(Lu et al. 2018)	-	-
You only search once: Single Shot Neural Architecture Search via Direct Sparse Optimization(Zhang et al. 2018)	-	-
Automatically Evolving CNN Architectures Based on Blocks(Sun et al. 2018) accepted at accepted by IEEE Transactions on Neural Networks and Learning Systems	-	-
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints(Hundt et al. 2018) accepted at IROS’19	-	-
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells(Nekrasov et al. 2018) accepted at CVPR’19	Image Segmentation	-
Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization(van Stein et al. 2018)	-	-
Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks(Mitschke et al. 2018)	-	-
Searching Toward Pareto-Optimal Device-Aware Neural Architectures(Cheng et al. 2018)	-	-
Neural Architecture Optimization(Luo et al. 2018) accepted at NeurIPS’18	-	-
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks(Chen et al. 2018)	-	-
Neural Architecture Search: A Survey(Elsken et al. 2018)	-	-
BlockQNN: Efficient Block-wise Neural Network Architecture Generation(Zhong et al. 2018)	-	-
Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification(Sunet al. 2018)	-	-
Reinforced Evolutionary Neural Architecture Search(Chen et al. 2018) accepted at CVPR’19	-	-
Teacher Guided Architecture Search(Bashivan et al. 2018)	-	-
Efficient Progressive Neural Architecture Search(Perez-Rua et al. 2018)	-	-
MnasNet: Platform-Aware Neural Architecture Search for Mobile(Tan et al. 2018) accepted at CVPR’19	-	-
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search(Zela et al. 2018)	-	-
Automatically Designing CNN Architectures for Medical Image Segmentation(Mortazi and Bagci 2018)	Medical Image Segmentation	-
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning(Hsu et al. 2018)	-	-
Path-Level Network Transformation for Efficient Architecture Search(Cai et al. 2018) accepted at ICML’18	-	-
Lamarckian Evolution of Convolutional Neural Networks(Prellberg and Kramer, 2018)	-	-
Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations(Wistuba, 2018)	-	-
DARTS: Differentiable Architecture Search(Liu et al. 2018) accepted at ICLR’19	-	-
Constructing Deep Neural Networks by Bayesian Network Structure Learning(Rohekar et al. 2018)	-	-
Resource-Efficient Neural Architect(Zhou et al. 2018)	-	-
Efficient Neural Architecture Search with Network Morphism(Jin et al. 2018)	-	-
TAPAS: Train-less Accuracy Predictor for Architecture Search(Istrate et al. 2018)	-	-
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search(Wang et al 2018) accepted at AAAI’20	-	-
Multi-objective Architecture Search for CNNs(Elsken et al. 2018)	-	-
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning(Huang et al 2018)	-	-
Evolutionary Architecture Search For Deep Multitask Networks(Liang et al. 2018)	-	-
From Nodes to Networks: Evolving Recurrent Neural Networks(Rawal et al. 2018)	-	-
Neural Architecture Construction using EnvelopeNets(Kamath et al. 2018)	-	-
Transfer Automatic Machine Learning(Wong et al. 2018)	-	-
Neural Architecture Search with Bayesian Optimisation and Optimal Transport(Kandasamy et al. 2018)	-	-
Efficient Neural Architecture Search via Parameter Sharing(Pham et al. 2018) accepted at ICML’18	-	-
Regularized Evolution for Image Classifier Architecture Search(Real et al. 2018)	-	-
Effective Building Block Design for Deep Convolutional Neural Networks using Search(Dutta et al. 2018)	-	-
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning(Wang et al. 2018)	-	-
Memetic Evolution of Deep Neural Networks(Lorenzo and Nalepa 2018)	-	-
Understanding and Simplifying One-Shot Architecture Search(Bender et al. 2018) accepted at ICML’18	-	-
Differentiable Neural Network Architecture Search(Shin et al. 2018) accepted at ICLR’18 workshop	-	-
PPP-Net: Platform-aware progressive search for pareto-optimal neural architectures(Dong et al. 2018) accepted at ICLR’18 workshop	-	-
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks(Hinz et al. 2018)	-	-
Gitgraph – From Computational Subgraphs to Smaller Architecture search spaces(Bennani-Smires et al. 2018)	-	-

2017

Title	Tags	Code
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning(Ashok et al. 2017) accepted at ICLR’18	-	-
Genetic CNN(Xie and Yuille, 2017) accepted at ICCV’17	-	-
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks(Gordon et al. 2017)	-	-
MaskConnect: Connectivity Learning by Gradient Descent(Ahmed and Torresani. 2017) accepted at ECCV’18	-	-
A Flexible Approach to Automated RNN Architecture Generation(Schrimpf et al. 2017)	-	-
DeepArchitect: Automatically Designing and Training Deep Architectures(Negrinho and Gordon 2017)	-	-
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures(Suganuma et al. 2017) accepted at GECCO’17	-	-
Practical Block-wise Neural Network Architecture Generation(Zhong et al. 2017) accepted at CVPR’18	-	-
Accelerating Neural Architecture Search using Performance Prediction(Baker et al. 2017) accepted at NeurIPS worshop on Meta-Learning 2017	-	-
Large-Scale Evolution of Image Classifiers(Real et al. 2017) accepted at ICML’17	-	-
Hierarchical Representations for Efficient Architecture Search(Liu et al. 2017) accepted at ICLR’18	-	-
Neural Optimizer Search with Reinforcement Learning(Bello et al. 2017)	-	-
Progressive Neural Architecture Search(Liu et al. 2017) accepted at ECCV’18	-	-
Learning Transferable Architectures for Scalable Image Recognition(Zoph et al. 2017) accepted at CVPR’18	-	-
Simple And Efficient Architecture Search for Convolutional Neural Networks(Elsken et al. 2017) accepted at NeurIPS workshop on Meta-Learning’17	-	-
Bayesian Optimization Combined with Incremental Evaluation for Neural Network Architecture Optimization(Wistuba, 2017)	-	-
Finding Competitive Network Architectures Within a Day Using UCT(Wistuba 2017)	-	-
Hyperparameter Optimization: A Spectral Approach(Hazan et al. 2017)	-	-
SMASH: One-Shot Model Architecture Search through HyperNetworks(Brock et al. 2017) accepted at NeurIPS workshop on Meta-Learning’17	-	-
Efficient Architecture Search by Network Transformation(Cai et al. 2017) accepted at AAAI’18	-	-
Modularized Morphing of Neural Networks(Wei et al. 2017)	-	-

2016

Title	Tags	Code
Towards Automatically-Tuned Neural Networks(Mendoza et al. 2016) accepted at ICML AutoML workshop	-	-
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization(Smithson et al. 2016)	-	-
AdaNet: Adaptive Structural Learning of Artificial Neural Networks(Cortes et al. 2016)	-	-
Network Morphism(Wei et al. 2016)	-	-
Convolutional Neural Fabrics(Saxena and Verbeek 2016) accepted at NeurIPS’16	-	-
CMA-ES for Hyperparameter Optimization of Deep Neural Networks(Loshchilov and Hutter 2016)	-	-
Designing Neural Network Architectures using Reinforcement Learning(Baker et al. 2016) accepted at ICLR’17	-	-
Neural Architecture Search with Reinforcement Learning(Zoph and Le. 2016) accepted at ICLR’17	-	-
Learning curve prediction with Bayesian Neural Networks(Klein et al. 2017: accepted at ICLR’17)	-	-
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization(Li et al. 2016)	-	-

1988-2015

Title	Tags	Code
Net2Net: Accelerating Learning via Knowledge Transfer(Chen et al. 2015) accepted at ICLR’16	-	-
Optimizing deep learning hyper-parameters through an evolutionary algorithm(Young et al. 2015)	-	-
Practical Bayesian Optimization of Machine Learning Algorithms(Snoek et al. 2012) accepted at NeurIPS’12	-	-
A Hypercube-based Encoding for Evolving large-scale Neural Networks(Stanley et al. 2009)	-	-
Neuroevolution: From Architectures to Learning(Floreano et al. 2008) accepted at Evolutionary Intelligence’08	-	-
Evolving Neural Networks through Augmenting Topologies(Stanley and Miikkulainen, 2002) accepted at Evolutionary Computation’02	-	-
Evolving Artificial Neural Networks(Yao, 1999) accepted at IEEE	-	-
An Evolutionary Algorithm that Constructs Recurrent Neural Networks(Angeline et al. 1994)	-	-
Designing Neural Networks Using Genetic Algorithms with Graph Generation System(Kitano, 1990)	-	-
Designing Neural Networks using Genetic Algorithms(Miller et al. 1989) accepted at ICGA’89	-	-
The Cascade-Correlation Learning Architecture(Fahlman and Leblere, 1989) accepted at NeurIPS’89	-	-
Self Organizing Neural Networks for the Identification Problem(Tenorio and Lee, 1988) accepted at NeurIPS’88	-	-

Awesome Neural Architecture Search Papers

Table of Contents

Tasks

Medical

Image_Segmentation

Model_Compression

Multi-objective_Search

Object_Detection

GAN

Image_Translator

Video_Models

GNN

Unsupervised

Binary_Networks

CTR

Multimodal_Learning

Federated_Learning

Speech_Recognition

Benchmark

Remote_Sensing

3D_Deep_Learning

Scene_Text_Recognition

NLP

Private_Inference

Imitation_Learning

Time_Series

Semantic_Segmentation

Distributed_System

Autonomous_Driving

Meta-learning

Language_Modeling

Image_Denoising

Image_Recognition

2020

2019

2018

2017

2016

1988-2015

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