Awesome Multi-task Learning

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Survey & Study
Benchmarks & Code
Papers
Awesome Multi-domain Multi-task Learning
Workshops
Online Courses
Related awesome list

Survey & Study

Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types (TPAMI, 2022) [paper]
Multi-Task Learning for Dense Prediction Tasks: A Survey (TPAMI, 2021) [paper] [code]
A Survey on Multi-Task Learning (TKDE, 2021) [paper]
Multi-Task Learning with Deep Neural Networks: A Survey (arXiv, 2020) [paper]
Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018, Best Paper) [paper] [dataset]
A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks (IEEE Access, 2019) [paper]
An Overview of Multi-Task Learning in Deep Neural Networks (arXiv, 2017) [paper]

Benchmarks & Code

Benchmarks

Dense Prediction Tasks

[NYUv2] Indoor Segmentation and Support Inference from RGBD Images (ECCV, 2012) [paper] [dataset]
[Cityscapes] The Cityscapes Dataset for Semantic Urban Scene Understanding (CVPR, 2016) [paper] [dataset]
[PASCAL-Context] The Role of Context for Object Detection and Semantic Segmentation in the Wild (CVPR, 2014) [paper] [dataset]
[Taskonomy] Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018 [best paper]) [paper] [dataset]
[KITTI] Vision meets robotics: The KITTI dataset (IJRR, 2013) [paper] dataset
[SUN RGB-D] SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (CVPR 2015) [paper] [dataset]
[BDD100K] BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (CVPR, 2020) [paper] [dataset]
[Omnidata] Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [paper] [project]
Cityscapes-3D Joint 2D-3D Multi-task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation. [dataset and code]

Image Classification

[Meta-dataset] Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (ICLR, 2020) [paper] [dataset]
[Visual Domain Decathlon] Learning multiple visual domains with residual adapters (NeurIPS, 2017) [paper] [dataset]
[CelebA] Deep Learning Face Attributes in the Wild (ICCV, 2015) [paper] [dataset]

Code

[Multi-Task-Transformer]: Transformer for Multi-task Learning including dense prediction problems and 3D detection on Cityscapes.
[Multi-Task-Learning-PyTorch]: Multi-task Dense Prediction.
[Auto-λ]: Multi-task Dense Prediction, Robotics.
[UniversalRepresentations]: Multi-task Dense Prediction (including different loss weighting strategies), Multi-domain Classification, Cross-domain Few-shot Learning.
[MTAN]: Multi-task Dense Prediction, Multi-domain Classification.
[ASTMT]: Multi-task Dense Prediction.
[LibMTL]: Multi-task Dense Prediction.
[MTPSL]: Multi-task Partially-supervised Learning for Dense Prediction.
[Resisual Adapater]: Multi-domain Classification.

Papers

2024

Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning (arXiv, 2024) [paper] [code]
Representation Surgery for Multi-Task Model Merging (arXiv, 2024) [paper] [code]
Multi-task Learning with 3D-Aware Regularization (ICLR, 2024) [paper] [code]
AdaMerging: Adaptive Model Merging for Multi-Task Learning (ICLR, 2024) [paper] [code]
ZipIt! Merging Models from Different Tasks without Training (ICLR, 2024) [paper] [code]
Denoising Task Routing for Diffusion Models (ICLR, 2024) [paper] [code]
Active Learning with Task Consistency and Diversity in Multi-Task Networks (WACV, 2024) [paper] [code]

2023

Addressing Negative Transfer in Diffusion Models (Neurips, 2023) [paper] [code]
Rethinking of Feature Interaction for Multi-task Learning on Dense Prediction (arXiv, 2023) [paper]
PolyMaX: General Dense Prediction with Mask Transformer (arXiv, 2023) [paper] [code]
Challenging Common Assumptions in Multi-task Learning (arXiv, 2023) [paper]
Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data (BMVC, 2023) [paper] [code]
Factorized Tensor Networks for Multi-task and Multi-domain Learning (arXiv, 2023) [paper]
UMT-Net: A Uniform Multi-Task Network with Adaptive Task Weighting (TIV, 2023) [paper]
Label Budget Allocation in Multi-Task Learning (arXiv, 2023) [paper]
Efficient Controllable Multi-Task Architectures (arXiv, 2023) [paper]
Foundation Model is Efficient Multimodal Multitask Model Selector (arXiv, 2023) [paper] [code]
Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction (arXiv, 2023) [paper] [code]
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts (ICCV, 2023) [paper] [code]
Deep Multitask Learning with Progressive Parameter Sharing (ICCV, 2023) [paper]
Achievement-based Training Progress Balancing for Multi-Task Learning (ICCV, 2023) [paper] [code]
Multi-Task Learning with Knowledge Distillation for Dense Prediction (ICCV, 2023) [paper]
Vision Transformer Adapters for Generalizable Multitask Learning (ICCV, 2023) [paper] [code]
TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts (ICCV, 2023) [paper]
Prompt Guided Transformer for Multi-Task Dense Prediction (arXiv, 2023) [paper]
Auxiliary Learning as an Asymmetric Bargaining Game (ICML, 2023) [paper] [code]
Learning to Modulate pre-trained Models in RL (arXiv, 2023) [paper] [code]
[InvPT++]: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding (arXiv, 2023) [paper] [code]
FAMO: Fast Adaptive Multitask Optimization (arXiv, 2023) [paper] [code]
Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks (arXiv, 2023) [paper]
DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning (arXiv, 2023) [paper]
Planning-oriented Autonomous Driving (CVPR, 2023, Best Paper) [paper] [code]
MDL-NAS: A Joint Multi-domain Learning Framework for Vision Transformer (CVPR, 2023) [paper]
Hierarchical Prompt Learning for Multi-Task Learning (CVPR, 2023) [paper]
Independent Component Alignment for Multi-Task Learning (CVPR, 2023) [paper] [code]
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning (TMLR, 2023) [paper] [code]
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning (arXiv, 2023) [paper]
ESSR: Evolving Sparse Sharing Representation for Multi-task Learning (arXiv, 2023) [paper]
AutoTaskFormer: Searching Vision Transformers for Multi-task Learning (arXiv, 2023) [paper]
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations (arXiv, 2023) [paper]
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision (arXiv, 2023) [paper]
Efficient Computation Sharing for Multi-Task Visual Scene Understanding (arXiv, 2023) [paper]
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners (CVPR, 2023) [paper] [code]
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives (CVPR, 2023) [paper] [code]
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach (ICLR, 2023) [paper]
UNIVERSAL FEW-SHOT LEARNING OF DENSE PREDIC- TION TASKS WITH VISUAL TOKEN MATCHING (ICLR, 2023) [paper]
TASKPROMPTER: SPATIAL-CHANNEL MULTI-TASK PROMPTING FOR DENSE SCENE UNDERSTANDING (ICLR, 2023) [paper] [code] [dataset]
Contrastive Multi-Task Dense Prediction (AAAI 2023) [paper]
Composite Learning for Robust and Effective Dense Predictions (WACV, 2023) [paper]
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search (WACV, 2023) [paper]

2022

RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction (arXiv, 2022) [paper]
LEARNING USEFUL REPRESENTATIONS FOR SHIFTING TASKS AND DISTRIBUTIONS (arXiv, 2022) [paper]
Sub-Task Imputation via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data (ACM CIKM, 2022) [paper]
Multi-Task Meta Learning: learn how to adapt to unseen tasks (arXiv, 2022) [paper]
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design (NeurIPS, 2022) [paper] [code]
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning (NeurIPS, 2022) [paper] [code]
Association Graph Learning for Multi-Task Classification with Category Shifts (NeurIPS, 2022) [paper] [code]
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? (NeurIPS, 2022) [paper]
Task Discovery: Finding the Tasks that Neural Networks Generalize on (NeurIPS, 2022) [paper]
[Auto-λ] Auto-λ: Disentangling Dynamic Task Relationships (TMLR, 2022) [paper] [code]
[Universal Representations] Universal Representations: A Unified Look at Multiple Task and Domain Learning (arXiv, 2022) [paper] [code]
MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning (ECCV, 2022) [paper]
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space (ECCV, 2022) [paper] [code]
Factorizing Knowledge in Neural Networks (ECCV, 2022) [paper] [code]
[InvPT] Inverted Pyramid Multi-task Transformer for Dense Scene Understanding (ECCV, 2022) [paper] [code]
[MultiMAE] MultiMAE: Multi-modal Multi-task Masked Autoencoders (ECCV, 2022) [paper] [code]
A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity (ICML, 2022) [paper]
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization (ICML, 2022) [paper]
Active Multi-Task Representation Learning (ICML, 2022) [paper]
Generative Modeling for Multi-task Visual Learning (ICML, 2022) [paper] [code]
Multi-Task Learning as a Bargaining Game (ICML, 2022) [paper] [code]
Multi-Task Learning with Multi-query Transformer for Dense Prediction (arXiv, 2022) [paper]
[Gato] A Generalist Agent (arXiv, 2022) [paper]
[MTPSL] Learning Multiple Dense Prediction Tasks from Partially Annotated Data (CVPR, 2022, Best Paper Finalist) [paper] [code]
[TSA] Cross-domain Few-shot Learning with Task-specific Adapters (CVPR, 2022) [paper] [code]
[OMNIVORE] OMNIVORE: A Single Model for Many Visual Modalities (CVPR, 2022) [paper] [code]
Task Adaptive Parameter Sharing for Multi-Task Learning (CVPR, 2022) [paper]
Controllable Dynamic Multi-Task Architectures (CVPR, 2022) [paper] [code]
[SHIFT] SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation (CVPR, 2022) [paper] [code]
DiSparse: Disentangled Sparsification for Multitask Model Compression (CVPR, 2022) [paper] [code]
[MulT] MulT: An End-to-End Multitask Learning Transformer (CVPR, 2022) [paper] [code]
Sound and Visual Representation Learning with Multiple Pretraining Tasks (CVPR, 2022) [paper]
Medusa: Universal Feature Learning via Attentional Multitasking (CVPR Workshop, 2022) [paper]
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems (arXiv, 2022) [paper] [code]
Combining Modular Skills in Multitask Learning (arXiv, 2022) [paper]
Visual Representation Learning over Latent Domains (ICLR, 2022) [paper]
ADARL: What, Where, and How to Adapt in Transfer Reinforcement Learning (ICLR, 2022) [paper] [code]
Towards a Unified View of Parameter-Efficient Transfer Learning (ICLR, 2022) [paper] [code]
[Rotograd] Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning (ICLR, 2022) [paper] [code]
Relational Multi-task Learning: Modeling Relations Between Data and Tasks (ICLR, 2022) [paper]
Weighted Training for Cross-task Learning (ICLR, 2022) [paper] [code]
Semi-supervised Multi-task Learning for Semantics and Depth (WACV, 2022) [paper]
In Defense of the Unitary Scalarization for Deep Multi-Task Learning (arXiv, 2022) [paper]

2021

Variational Multi-Task Learning with Gumbel-Softmax Priors (NeurIPS, 2021) [paper] [code]
Efficiently Identifying Task Groupings for Multi-Task Learning (NeurIPS, 2021) [paper]
[CAGrad] Conflict-Averse Gradient Descent for Multi-task Learning (NeurIPS, 2021) [paper] [code]
A Closer Look at Loss Weighting in Multi-Task Learning (arXiv, 2021) [paper]
Exploring Relational Context for Multi-Task Dense Prediction (ICCV, 2021) [paper] [code]
Multi-Task Self-Training for Learning General Representations (ICCVW, 2021) [paper]
Task Switching Network for Multi-task Learning (ICCV, 2021) [paper] [code]
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [paper] [project]
Robustness via Cross-Domain Ensembles (ICCV, 2021) [paper] [code]
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (ICCV, 2021) [paper] [code]
[URL] Universal Representation Learning from Multiple Domains for Few-shot Classification (ICCV, 2021) [paper] [code]
[tri-M] A Multi-Mode Modulator for Multi-Domain Few-Shot Classification (ICCV, 2021) [paper] [code]
MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach (ICCV Workshop, 2021) [paper]
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection (arXiv, 2021) [paper]
A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation (CinC, 2021) [paper] [code]
Multi-Task Reinforcement Learning with Context-based Representations (ICML, 2021) [paper]
[FLUTE] Learning a Universal Template for Few-shot Dataset Generalization (ICML, 2021) [paper] [code]
Towards a Unified View of Parameter-Efficient Transfer Learning (arXiv, 2021) [paper]
UniT: Multimodal Multitask Learning with a Unified Transformer (arXiv, 2021) [paper]
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation (CVPR, 2021) [paper] [code]
CompositeTasking: Understanding Images by Spatial Composition of Tasks (CVPR, 2021) [paper] [code]
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR, 2021) [paper]
Taskology: Utilizing Task Relations at Scale (CVPR, 2021) [paper]
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation (CVPR, 2021) [paper] [code]
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (arXiv, 2021) [paper] [code]
Counter-Interference Adapter for Multilingual Machine Translation (Findings of EMNLP, 2021) [paper]
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (ICLR) [paper] [code]
[Gradient Vaccine] Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models (ICLR, 2021) [paper]
[IMTL] Towards Impartial Multi-task Learning (ICLR, 2021) [paper]
Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach (ICLR, 2021) [paper]
[URT] A Universal Representation Transformer Layer for Few-Shot Image Classification (ICLR, 2021) [paper] [code]
Flexible Multi-task Networks by Learning Parameter Allocation (ICLR Workshop, 2021) [paper]
Multi-Loss Weighting with Coefficient of Variations (WACV, 2021) [paper] [code]

2020

Multi-Task Reinforcement Learning with Soft Modularization (NeurIPS, 2020) [paper] [code]
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS, 2020) [paper] [code]
[GradDrop] Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (NeurIPS, 2020) [paper] [code]
[PCGrad] Gradient Surgery for Multi-Task Learning (NeurIPS, 2020) [paper] [tensorflow] [pytorch]
On the Theory of Transfer Learning: The Importance of Task Diversity (NeurIPS, 2020) [paper]
A Study of Residual Adapters for Multi-Domain Neural Machine Translation (WMT, 2020) [paper]
Multi-Task Adversarial Attack (arXiv, 2020) [paper]
Automated Search for Resource-Efficient Branched Multi-Task Networks (BMVC, 2020) [paper] [code]
Branched Multi-Task Networks: Deciding What Layers To Share (BMVC, 2020) [paper]
MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning (ECCV, 2020) [paper] [code]
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference (ECCV, 2020) [paper] [code]
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification (ECCV, 2020) [paper] [code]
Multitask Learning Strengthens Adversarial Robustness (ECCV 2020) [paper] [code]
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning (ECCV, 2020) [paper] [code]
[KD4MTL] Knowledge Distillation for Multi-task Learning (ECCV Workshop) [paper] [code]
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning (CVPR, 2020) [paper] [code]
Robust Learning Through Cross-Task Consistency (CVPR, 2020) [paper] [code]
12-in-1: Multi-Task Vision and Language Representation Learning (CVPR, 2020) paper [code]
A Multi-task Mean Teacher for Semi-supervised Shadow Detection (CVPR, 2020) [paper] [code]
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer (EMNLP, 2020) [paper]
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models (EMNLP, 2020) [paper] [code]
Effcient Continuous Pareto Exploration in Multi-Task Learning (ICML, 2020) [paper] [code]
Which Tasks Should Be Learned Together in Multi-task Learning? (ICML, 2020) [paper] [code]
Learning to Branch for Multi-Task Learning (ICML, 2020) [paper]
Partly Supervised Multitask Learning (ICMLA, 2020) paper
Understanding and Improving Information Transfer in Multi-Task Learning (ICLR, 2020) [paper]
Measuring and Harnessing Transference in Multi-Task Learning (arXiv, 2020) [paper]
Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition (arXiv, 2020) [paper]
Learning Sparse Sharing Architectures for Multiple Tasks (AAAI, 2020) [paper]
AdapterFusion: Non-Destructive Task Composition for Transfer Learning (arXiv, 2020) [paper]

2019

Adaptive Auxiliary Task Weighting for Reinforcement Learning (NeurIPS, 2019) [paper]
Pareto Multi-Task Learning (NeurIPS, 2019) [paper] [code]
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains (NeurIPS, 2019) [paper]
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (NeurIPS, 2019) [paper] [code]
[Orthogonal] Regularizing Deep Multi-Task Networks using Orthogonal Gradients (arXiv, 2019) [paper]
Many Task Learning With Task Routing (ICCV, 2019) [paper] [code]
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels (ICCV, 2019) [paper]
Deep Elastic Networks with Model Selection for Multi-Task Learning (ICCV, 2019) [paper] [code]
Feature Partitioning for Efficient Multi-Task Architectures (arXiv, 2019) [paper] [code]
Task Selection Policies for Multitask Learning (arXiv, 2019) [paper]
BAM! Born-Again Multi-Task Networks for Natural Language Understanding (ACL, 2019) [paper] [code]
OmniNet: A unified architecture for multi-modal multi-task learning (arXiv, 2019) [paper]
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (CVPR, 2019) [paper] [code]
[MTAN + DWA] End-to-End Multi-Task Learning with Attention (CVPR, 2019) [paper] [code]
Attentive Single-Tasking of Multiple Tasks (CVPR, 2019) [paper] [code]
Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation (CVPR, 2019) [paper]
Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning (CVPR, 2019) [paper] [code]
[Geometric Loss Strategy (GLS)] MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning (CVPR Workshop, 2019) [paper]
Parameter-Efficient Transfer Learning for NLP (ICML, 2019) [paper]
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ICML, 2019) [paper] [code]
Tasks Without Borders: A New Approach to Online Multi-Task Learning (ICML Workshop, 2019) [paper]
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (NACCL, 2019) [paper] [code]
Multi-Task Deep Reinforcement Learning with PopArt (AAAI, 2019) [paper]
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (AAAI, 2019) [paper]
Latent Multi-task Architecture Learning (AAAI, 2019) [paper] [[code](https://github.com/ sebastianruder/sluice-networks)]
Multi-Task Deep Neural Networks for Natural Language Understanding (ACL, 2019) [paper]

2018

Learning to Multitask (NeurIPS, 2018) [paper]
[MGDA] Multi-Task Learning as Multi-Objective Optimization (NeurIPS, 2018) [paper] [code]
Adapting Auxiliary Losses Using Gradient Similarity (arXiv, 2018) [paper] [code]
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV, 2018) [paper] [code]
Dynamic Task Prioritization for Multitask Learning (ECCV, 2018) [paper]
A Modulation Module for Multi-task Learning with Applications in Image Retrieval (ECCV, 2018) [paper]
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (KDD, 2018) [paper]
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage (IJCAI, 2018) [paper] [code]
Efficient Parametrization of Multi-domain Deep Neural Networks (CVPR, 2018) [paper] [code]
PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (CVPR, 2018) [paper]
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks (CVPR, 2018) [paper]
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR, 2018) [paper] [code]
[Uncertainty] Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (CVPR, 2018) [paper]
Deep Asymmetric Multi-task Feature Learning (ICML, 2018) [paper]
[GradNorm] GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ICML, 2018) [paper]
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back (ICML, 2018) [paper]
Gradient Adversarial Training of Neural Networks (arXiv, 2018) [paper]
Auxiliary Tasks in Multi-task Learning (arXiv, 2018) [paper]
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (ICLR, 2018) [paper] [code
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (ICLR, 2018) [paper]

2017

Learning multiple visual domains with residual adapters (NeurIPS, 2017) [paper] [code]
Learning Multiple Tasks with Multilinear Relationship Networks (NeurIPS, 2017) [paper] [code]
Federated Multi-Task Learning (NeurIPS, 2017) [paper] [code]
Multi-task Self-Supervised Visual Learning (ICCV, 2017) [paper]
Adversarial Multi-task Learning for Text Classification (ACL, 2017) [paper]
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (CVPR, 2017) [paper]
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification (CVPR, 2017) [paper]
Modular Multitask Reinforcement Learning with Policy Sketches (ICML, 2017) [paper] [code]
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (ICML, 2017) [paper] [code]
One Model To Learn Them All (arXiv, 2017) [paper] [code]
[AdaLoss] Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing (arXiv, 2017) [paper]
Deep Multi-task Representation Learning: A Tensor Factorisation Approach (ICLR, 2017) [paper] [code]
Trace Norm Regularised Deep Multi-Task Learning (ICLR Workshop, 2017) [paper] [code]
When is multitask learning effective? Semantic sequence prediction under varying data conditions (EACL, 2017) [paper] [code]
Identifying beneficial task relations for multi-task learning in deep neural networks (EACL, 2017) [paper]
PathNet: Evolution Channels Gradient Descent in Super Neural Networks (arXiv, 2017) [paper] [code]
Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classiﬁcation (AAAI, 2017) [paper]

2016 and earlier

Learning values across many orders of magnitude (NeurIPS, 2016) [paper]
Integrated Perception with Recurrent Multi-Task Neural Networks (NeurIPS, 2016) [paper]
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives (arXiv, 2016) [paper]
Progressive Neural Networks (arXiv, 2016) [paper]
Deep multi-task learning with low level tasks supervised at lower layers (ACL, 2016) [paper]
[Cross-Stitch] Cross-Stitch Networks for Multi-task Learning (CVPR,2016) [paper] [code]
Asymmetric Multi-task Learning based on Task Relatedness and Confidence (ICML, 2016) [paper]
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving (arXiv, 2016) [paper] [code]
A Unified Perspective on Multi-Domain and Multi-Task Learning (ICLR, 2015) [paper]
Facial Landmark Detection by Deep Multi-task Learning (ECCV, 2014) [paper] [code]
Learning Task Grouping and Overlap in Multi-task Learning (ICML, 2012) [paper]
Learning with Whom to Share in Multi-task Feature Learning (ICML, 2011) [paper]
Semi-Supervised Multi-Task Learning with Task Regularizations (ICDM, 2009) [paper]
Semi-Supervised Multitask Learning (NeurIPS, 2008) [paper]
Multitask Learning (1997) [paper]

WeiHongLee / Awesome-Multi-Task-Learning

Awesome Multi-task Learning

Table of Contents

Survey & Study

Benchmarks & Code

Dense Prediction Tasks

Image Classification

Papers

2024

2023

2022

2021

2020

2019

2018

2017

2016 and earlier

Awesome Multi-domain Multi-task Learning

Workshops

Online Courses

Related awesome list

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