Deep-Learning-Learning-List

Some courses or papers I have learned.

Table of Contents

• Online Courses
• Papers
  • Survey
  • Image Classification
    • Backbone
    • Visualization
    • Cls Others
  • Video
  • Object Detection
    • One-Stage
    • Two-Stage
  • Segmentation
    • Semantic Segmentation
    • Real time Semantic Segmentation
    • Point Cloud
    • Instance segmentation
    • Panoptic segmentation
  • Domain Adaptation
  • Nautre Language Process
  • EEG
    • Generic
    • Sleep Stage Classification
• Software and Skills

Online Courses

• Andrew Ng. Machine learning. Stanford.
• Andrew Ng. Deep Learning Specialization. deepleanirng.ai

Papers

Survey

• LeCun Y, Bengio Y, Hinton G. Deep learning[J]. (Nature 2015) [paper]

• Ruder S. An overview of multi-task learning in deep neural networks[J]. (arXiv 2017) [paper]

• Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A review on deep learning techniques applied to semantic segmentation[J]. (arXiv 2017) [Segmentation]

• Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: A survey[J]. (arXiv 2018) [Detection]

• Hong Y, Hwang U, Yoo J, et al. How Generative Adversarial Networks and Their Variants Work: An Overview[J]. (CSUR 2019) [paper]

Image Classification

Backbone

• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]. (ICLR 2015) [VGGNet]

• Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. (CVPR 2015) [Inception]

• He K, Zhang X, Ren S, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]. (ICCV 2015) [PReLU]

• Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. (ICML 2015) [BatchNorm]

• Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]. (CVPR 2016) [InceptionV3]

• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. (CVPR 2016) [ResNet]

• He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks[C]. (ECCV 2016) [ResNetV2]

• Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]. (CVPR 2017) [ResNeXt]

• Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]. (CVPR 2017) [DenseNet]

• Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. (CVPR 2018) [SE-Net]

• Sun S, Pang J, Shi J, et al. FishNet: A versatile backbone for image, region, and pixel level prediction[C]. (NIPS 2018) [FishNet]

Visualization

• Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]. (ECCV 2014) [paper]

• Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]. (ICML 2015) [paper]

• Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization[C]. (CVPR 2016) [CAM]

• Geirhos R, Rubisch P, Michaelis C, et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness[C]. (ICLR 2019) [shapeVStexture]

Cls Others

• Lin M, Chen Q, Yan S. Network in network[C]. (2013.12) [Conv1x1]

• Springenberg J T, Dosovitskiy A, Brox T, et al. Striving for simplicity: The all convolutional net[C] (ICLR workshop 2015) [paper]

• Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[C]. (NIPS 2015) [STN]

• Yu Q, Wang J, Zhang S, et al. Combining local and global hypotheses in deep neural network for multi-label image classification[J]. (Neurocomputing 2017) [2015.08]

• Chollet F. Xception: Deep learning with depthwise separable convolutions[C]. (CVPR 2017) [Xception]

• Howard A G, Zhu M, Chen B, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[C]. (2017.04) [MobileNet]

• Wu Y, He K. Group normalization[C]. (ECCV 2018) [GroupNorm]

• Xie J, He T, Zhang Z, et al. Bag of tricks for image classification with convolutional neural networks[C]. (CVPR 2019) [tricks]

Video

• Karpathy A, Toderici G, Shetty S, et al. Large-scale video classification with convolutional neural networks[C]. (CVPR 2014) [paper]

• Yue-Hei Ng J, Hausknecht M, Vijayanarasimhan S, et al. Beyond short snippets: Deep networks for video classification[C]. (CVPR 2015) [paper]

• Donahue J, Anne Hendricks L, Guadarrama S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]. (CVPR 2015) [paper]

• Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]. (CVPR 2018) [Non-local]

Object Detection

One-Stage

• Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]. (CVPR 2016) [YOLO]

• Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]. (ECCV 2016) [SSD]

• Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. (CVPR 2017) [YOLOv2]

• Redmon J, Farhadi A. YOLOv3: An incremental improvement[J]. (arXiv 2018) [YOLOv3]

• Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]. (ICCV 2017) [Focal Loss]

• Zhang S, Wen L, Bian X, et al. Single-shot refinement neural network for object detection[C]. (CVPR 2018) [RefineDet]

Two-Stage

• Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. (CVPR 2014) [R-CNN]

• Girshick R. Fast R-CNN[C]. (ICCV 2015) [Fast R-CNN]

• Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]. (NIPS 2015) [Faster R-CNN]

• Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining[C]. (CVPR 2016) [OHEM]

• Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]. (CVPR 2017) [FPN]

• He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]. (ICCV 2017) [Mask R-CNN]

Segmentation

Semantic Segmentation

• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. (CVPR 2015) [FCN]

• Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. (arXiv 2014) [DeepLabV1]

• Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation[C]. (MICCAI 2015) [U-Net]

• Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. (TPAMI 2017) [SegNet]

• Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J]. (arXiv 2015) [paper]

• Wu Z, Shen C, Hengel A. High-performance semantic segmentation using very deep fully convolutional networks[J]. (arXiv 2016) [OHEM]

• Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. (TPAMI 2018) [DeepLabV2]

• Lin G, Milan A, Shen C, et al. RefineNet: Multi-path refinement networks for high-resolution semantic segmentation[C]. (CVPR 2017) [RefineNet]

• Zhao H, Shi J, Qi X, et al. Pyramid scene parsing network[C]. (CVPR 2017) [PSPNet]

• Wang P, Chen P, Yuan Y, et al. Understanding convolution for semantic segmentation[C]. (WACV 2018) [DUC]

• Yu F, Koltun V, Funkhouser T. Dilated residual networks[C]. (CVPR 2017) [paper]

• Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[J]. (arXiv 2017) [DeepLabV3]

• Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]. (ECCV 2018) [DeepLabV3+]

• Ke T W, Hwang J J, Liu Z, et al. Adaptive Affinity Fields for Semantic Segmentation[C]. (ECCV 2018) [AAF]

• Zhang H, Dana K, Shi J, et al. Context encoding for semantic segmentation[C]. (CVPR 2018) [EncNet]

• Zhang Z, Zhang X, Peng C, et al. ExFuse: Enhancing feature fusion for semantic segmentation[C]. (ECCV 2018) [ExFuse]

• Yu C, Wang J, Peng C, et al. Learning a discriminative feature network for semantic segmentation[C]. (CVPR 2018) [DFN]

• Chen L C, Collins M, Zhu Y, et al. Searching for efficient multi-scale architectures for dense image prediction[C]. (NIPS 2018) [NAS Seg]

• Zhao H, Zhang Y, Liu S, et al. PSANet: Point-wise spatial attention network for scene parsing[C] (ECCV 2018) [PSANet]

• Fu J, Liu J, Tian H, et al. Dual attention network for scene segmentation[J]. (arXiv 2018) [DANet]

• Huang Z, Wang X, Huang L, et al. CCNet: Criss-Cross Attention for Semantic Segmentation[J]. (arXiv 2018) [CCNet]

Real time Semantic Segmentation

• Paszke A, Chaurasia A, Kim S, et al. ENet: A deep neural network architecture for real-time semantic segmentation[J]. (arXiv 2016) [ENet]

• Zhao H, Qi X, Shen X, et al. ICNet for real-time semantic segmentation on high-resolution images[C]. (ECCV 2018) [ICNet]

• Mehta S, Rastegari M, Caspi A, et al. ESPnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation[C]. (ECCV 2018) [ESPNet]

• Yu C, Wang J, Peng C, et al. BiSeNet: Bilateral segmentation network for real-time semantic segmentation[C] (ECCV 2018) [BiSeNet]

Point Cloud

• Qi C R, Su H, Mo K, et al. PointNet: Deep learning on point sets for 3d classification and segmentation[C]. (CVPR 2017) [PointNet]

• Qi C R, Yi L, Su H, et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[C] (NIPS 2017) [PointNet++]

Instance segmentation

• He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]. (ICCV 2017) [Mask R-CNN]

Panoptic segmentation

• Kirillov A, He K, Girshick R, et al. Panoptic segmentation[J]. (arXiv 2018) [metric]

• Yang T J, Collins M D, Zhu Y, et al. DeeperLab: Single-Shot Image Parser[J]. (arXiv 2019) [DeeperLab]

• Kirillov A, Girshick R, He K, et al. Panoptic Feature Pyramid Networks[J]. (arXiv 2019) [Panoptic FPN]

Domain Adaptation

• Tzeng E, Hoffman J, Zhang N, et al. Deep domain confusion: Maximizing for domain invariance[J]. (arXiv 2014) [paper]

• Ghifary M, Kleijn W B, Zhang M. Domain adaptive neural networks for object recognition[C]. (PRICAI 2014) [paper]

• Long M, Cao Y, Wang J, et al. Learning transferable features with deep adaptation networks[C]. (ICML 2015) [DAN]

• Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]. (ICML 2015) [DaNN]

• Li Y, Wang N, Shi J, et al. Adaptive Batch Normalization for practical domain adaptation[J]. (PR 2018) [AdapaBN]

Nautre Language Process

• Kim Y. Convolutional neural networks for sentence classification[C]. (EMNLP 2014) [TextCNN][code]

• Christopher Olah. Understanding LSTMs. (2015.08) [url]

• Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[C]. (ICLR 2018) [Attention]

• Gehring J, Auli M, Grangier D, et al. Convolutional sequence to sequence learning[C]. (ICML 2017) [ConvS2S]

• Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]. (NIPS 2017) [Transformer]

EEG

Generic EEG

Sleep Stage Classification

Software and Skills

Framework

• TensorFlow [install&docs]
• Keras [docs]
• PyTorch [install] [docs]

Skills

• python tutorial
• latex
• git