This repository contains my paper reading notes on deep learning and machine learning. New year resolution for 2019: read at least one paper a week!
| Topics | Description |
|---|---|
| DRL | Deep Reinforcement Learning |
| CLS | Classification |
| OD | Object Detection |
| InsSeg, SemSeg, PanSeg | Instance/Semantic/Panoptic Segmentation |
| Video | Video understanding |
| MI | Medical Imaging |
| GAN | Generative Adversarial Network |
| NIPS, CVPR, ICCV, ECCV, etc | Conference papers |
- Notes on Focal Loss and RetinaNet [Notes]
- Anchor Free [知乎]
- YOLO [知乎]
- RCNN, Fast RCNN, Faster RCNN, MaskRCNN[CSDN]
- Bag of Freebies for Training Object Detection Neural Networks [Notes]
- mixup: Beyond Empirical Risk Minimization [Notes] ICLR 2018
- Multi-view Convolutional Neural Networks for 3D Shape Recognition (MVCNN) [Notes] ICCV 2015 Does doing CNN on RGBD work?
- 3D ShapeNets: A Deep Representation for Volumetric Shapes [Notes] CVPR 2015
- Volumetric and Multi-View CNNs for Object Classification on 3D Data [Notes] CVPR 2016
- Group Normalization [Notes] ECCV 2018
- Spatial Transformer Networks [Notes]NIPS 2015
- Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset (I3D) [Notes]Video CVPR 2017
- Initialization Strategies of Spatio-Temporal Convolutional Neural Networks [Notes] Video
- Detect-and-Track: Efficient Pose Estimation in Videos [Notes] ICCV 2017 Video
- Deep Learning Based Rib Centerline Extraction and Labeling [Notes] MI MICCAI 2018
- SlowFast Networks for Video Recognition [Notes] Video
- Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) [Notes] CVPR 2017.
- Beyond the pixel plane: sensing and learning in 3D (blog, 中文版本)
- VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition (VoxNet) [Notes]
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation CVPR 2017 [Notes]
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space NIPS 2017 [Notes]
- Review of Geometric deep learning 几何深度学习前沿 (from 知乎) (Up to CVPR 2018)
- Human-level control through deep reinforcement learning (Nature DQN paper) [Notes] DRL
- Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection [Notes] MI
- Panoptic Segmentation [Notes] PanSeg
- Panoptic Feature Pyramid Networks [Notes] PanSeg
- Attention-guided Unified Network for Panoptic Segmentation [Notes] PanSeg
- Bag of Tricks for Image Classification with Convolutional Neural Networks [Notes] CLS
- Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes [Notes] DRL MI
- Deep Reinforcement Learning for Flappy Bird [Notes] DRL
- Long-Term Feature Banks for Detailed Video Understanding [Notes] Video
- Non-local Neural Networks [Notes] Video CVPR 2018
- Mask R-CNN
- Cascade R-CNN: Delving into High Quality Object Detection
- Focal Loss for Dense Object Detection (RetinaNet)
- Squeeze-and-Excitation Networks
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Deformable Convolutional Networks
- Learning Region Features for Object Detection
- Learning notes on Deep Learning
- List of Papers on Machine Learning
- Notes of Literature Review on CNN in CV This is the notes for all the papers in the recommended list here
- Notes of Literature Review (Others)
- Notes on how to set up DL/ML environment
- Useful setup notes
Here is the list of papers waiting to be read.
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (MobileNet)
- MobileNetV2: Inverted Residuals and Linear Bottlenecks (MobileNet v2)
- Xception: Deep Learning with Depthwise Separable Convolutions (Xception)
- SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving (SqueezeDet)
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
- ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness ICML 2019
- Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet (BagNet) blog ICML 2019
- A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
- Understanding deep learning requires rethinking generalization
- Learning Spatiotemporal Features with 3D Convolutional Networks (C3D) Video ICCV 2015
- AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
- Spatiotemporal Residual Networks for Video Action Recognition (decouple spatiotemporal) NIPS 2016
- Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks (P3D, decouple spatiotemporal) ICCV 2017
- A Closer Look at Spatiotemporal Convolutions for Action Recognition (decouple spatiotemporal) CVPR 2018
- Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification (decouple spatiotemporal) ECCV 2018
- Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? CVPR 2018
- nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
- Foveal fully convolutional nets for multi-organ segmentation
- Evaluation of deep learning methods for parotid gland segmentation from CT images
- DeepMedic for Brain Tumor Segmentation
- Attention U-Net: Learning Where to Look for the Pancreas
- Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
- 3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
- Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
- CBAM: Convolutional Block Attention Module
- Playing Atari with Deep Reinforcement Learning NIPS 2013
- Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scan
- An Artificial Agent for Robust Image Registration
- ** 3D-CNN：3D Convolutional Neural Networks for Landing Zone Detection from LiDAR**
- Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
- Orientation-boosted Voxel Nets for 3D Object Recognition (ORION) <BMVC 2017>
- GIFT: A Real-time and Scalable 3D Shape Search Engine CVPR 2016
- 3D Shape Segmentation with Projective Convolutional Networks (ShapePFCN)CVPR 2017
- Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
- Open3D: A Modern Library for 3D Data Processing
- Multimodal Deep Learning for Robust RGB-D Object Recognition IROS 2015
- ** Frustum PointNets for 3D Object Detection from RGB-D Data**
- ** FlowNet3D: Learning Scene Flow in 3D Point Clouds CVPR 2019
- Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling CVPR 2018
- PU-Net: Point Cloud Upsampling Network CVPR 2018
- Recurrent Slice Networks for 3D Segmentation of Point Clouds CVPR 2018
- SPLATNet: Sparse Lattice Networks for Point Cloud Processing CVPR 2018
- Dynamic Graph CNN for Learning on Point Clouds
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering NIPS 2016
- Semi-Supervised Classification with Graph Convolutional Networks ICLR 2017
- Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks NIPS 2017
- Graph Attention Networks ICLR 2018
- 3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection (3D SSD)
- Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models ICCV 2017
- PointCNN: Convolution On X-Transformed Points NIPS 2018
- PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
- ** Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis CVPR 2017