Summary: My Paper Reading Lists, Tutorials & Sharings

From Image Classification, Object Detection, Natural Language Processing (NLP), Self-Supervised Learning, Semi-Supervised Learning, Vision-Language, Generative Adversarial Network (GAN) to …

In this story, as the list is too long to be posted in each story, a list of my paper readings, tutorials and also sharings are posted here for convenience and will be updated from time to time.

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Thanks everyone for the support & the reading my stories.

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Actually, I wrote what I’ve learnt only. Reading a paper can consume hours or days. Sometimes, it is quite luxury to read a paper. I hope I can dig out some important points in the paper, or help reading the papers at a faster pace. But if there are some papers that you’re particularly interested in, it’s better to read the original papers for more detailed explanations. If there are something wrong, please also tell me. Thank you. (Sik-Ho Tsang @ Medium)

1. Computer Vision

1.1. Image Classification

1989-1998: [LeNet]
2012–2014: [AlexNet & CaffeNet] [Dropout] [Maxout] [NIN] [ZFNet] [SPPNet] [Distillation]
2015: [VGGNet] [Highway] [PReLU-Net] [STN] [DeepImage] [GoogLeNet / Inception-v1] [BN-Inception / Inception-v2]
2016: [SqueezeNet] [Inception-v3] [ResNet] [Pre-Activation ResNet] [RiR] [Stochastic Depth] [WRN] [Trimps-Soushen] [GELU] [Layer Norm, LN]
2017: [Inception-v4] [Xception] [MobileNetV1] [Shake-Shake] [Cutout] [FractalNet] [PolyNet] [ResNeXt] [DenseNet] [PyramidNet] [DRN] [DPN] [Residual Attention Network] [IGCNet / IGCV1] [Deep Roots]
2018: [RoR] [DMRNet / DFN-MR] [MSDNet] [ShuffleNet V1] [SENet] [NASNet] [MobileNetV2] [CondenseNet] [IGCV2] [IGCV3] [FishNet] [SqueezeNext] [ENAS] [PNASNet] [ShuffleNet V2] [BAM] [CBAM] [MorphNet] [NetAdapt] [mixup] [DropBlock] [Group Norm (GN)] [Pelee & PeleeNet] [DLA] [Swish]
2019: [ResNet-38] [AmoebaNet] [ESPNetv2] [MnasNet] [Single-Path NAS] [DARTS] [ProxylessNAS] [MobileNetV3] [FBNet] [ShakeDrop] [CutMix] [MixConv] [EfficientNet] [ABN] [SKNet] [CB Loss] [AutoAugment, AA] [BagNet] [Stylized-ImageNet] [FixRes] [SASA] [SE-WRN] [SGELU] [ImageNet-V2] [Bag of Tricks, ResNet-D]
2020: [Random Erasing (RE)] [SAOL] [AdderNet] [FixEfficientNet] [BiT] [RandAugment] [ImageNet-ReaL] [ciFAIR] [ResNeSt] [Batch Augment, BA] [Mish]
2021: [Learned Resizer] [Vision Transformer, ViT] [ResNet Strikes Back] [DeiT] [EfficientNetV2] [MLP-Mixer] [T2T-ViT] [Swin Transformer] [CaiT] [ResMLP] [ResNet-RS] [NFNet] [PVT, PVTv1] [CvT] [HaloNet] [TNT]

1.2. Unsupervised/Self-Supervised Learning

1993 [de Sa NIPS’93] 2008–2010 [Stacked Denoising Autoencoders] 2014 [Exemplar-CNN] 2015 [Context Prediction] [Wang ICCV’15] 2016 [Context Encoders] [Colorization] [Jigsaw Puzzles] 2017 [L³-Net] [Split-Brain Auto] [Motion Masks] [Doersch ICCV’17] 2018 [RotNet/Image Rotations] [DeepCluster] [CPC/CPCv1] [Instance Discrimination] 2019 [Ye CVPR’19] 2020 [CMC] [MoCo] [CPCv2] [PIRL] [SimCLR] [MoCo v2] [iGPT] [BoWNet] [BYOL] [SimCLRv2] 2021 [MoCo v3] [SimSiam]

1.3. Pretraining or Weakly/Semi-Supervised Learning

2004 [Entropy Minimization, EntMin] 2013 [Pseudo-Label (PL)] 2015 [Ladder Network, Γ-Model] 2016 [Sajjadi NIPS’16] 2017 [Mean Teacher] [PATE & PATE-G] [Π-Model, Temporal Ensembling] 2018 [WSL] [Oliver NeurIPS’18] 2019 [VAT] [Billion-Scale] [Label Propagation] [Rethinking ImageNet Pre-training] [MixMatch] [SWA & Fast SWA] 2020 [BiT] [Noisy Student] [SimCLRv2]

1.4. Weakly Supervised Object Localization (WSOL)

2014 [Backprop] 2016 [CAM] 2017 [Grad-CAM] [Hide-and-Seek] 2018 [Grad-CAM++] [ACoL] [SPG] 2019 [CutMix] [ADL] 2020 [Evaluating WSOL Right] [SAOL]

1.5. Object Detection

2014 [OverFeat] [R-CNN] 2015 [Fast R-CNN] [Faster R-CNN] [MR-CNN & S-CNN] [DeepID-Net] 2016 [OHEM] [CRAFT] [R-FCN] [ION] [MultiPathNet] [Hikvision] [GBD-Net / GBD-v1 & GBD-v2] [SSD] [YOLOv1] 2017 [NoC] [G-RMI] [TDM] [DSSD] [YOLOv2 / YOLO9000] [FPN] [RetinaNet] [DCN / DCNv1] [Light-Head R-CNN] [DSOD] [CoupleNet] 2018 [YOLOv3] [Cascade R-CNN] [MegDet] [StairNet] [RefineDet] [CornerNet] [Pelee & PeleeNet] 2019 [DCNv2] [Rethinking ImageNet Pre-training] [GRF-DSOD & GRF-SSD] [CenterNet] [Grid R-CNN] [NAS-FPN] [ASFF] [Bag of Freebies] [VoVNet/OSANet] [FCOS] [GIoU] 2020 [EfficientDet] [CSPNet] [YOLOv4] [SpineNet] [DETR] [Mish] 2021 [Scaled-YOLOv4] [PVT, PVTv1]

1.6. Semantic Segmentation

2015 [FCN] [DeconvNet] [DeepLabv1 & DeepLabv2] [CRF-RNN] [SegNet] [DPN] 2016 [ENet] [ParseNet] [DilatedNet] 2017 [DRN] [RefineNet] [ERFNet] [GCN] [PSPNet] [DeepLabv3] [LC] [FC-DenseNet] [IDW-CNN] [DIS] [SDN] [Cascade-SegNet & Cascade-DilatedNet] 2018 [ESPNet] [ResNet-DUC-HDC] [DeepLabv3+] [PAN] [DFN] [EncNet] [DLA] 2019 [ResNet-38] [C3] [ESPNetv2] [ADE20K] [Semantic FPN, Panoptic FPN] 2020 [DRRN Zhang JNCA’20] 2021 [PVT, PVTv1]

1.7. Instance Segmentation

2014–2015 [SDS] [Hypercolumn] [DeepMask] 2016 [SharpMask] [MultiPathNet] [MNC] [InstanceFCN] 2017 [FCIS] [Mask R-CNN] 2018 [MaskLab] [PANet] 2019 [DCNv2] [Rethinking ImageNet Pre-training] 2021 [PVT, PVTv1]

1.8. Panoptic Segmentation

2019 [PS] [UPSNet] [Semantic FPN, Panoptic FPN] 2020 [DETR]

1.9. Biomedical Image Segmentation

2015 [U-Net] 2016 [CUMedVision1] [CUMedVision2 / DCAN] [CFS-FCN] [U-Net+ResNet] [MultiChannel] [V-Net] [3D U-Net] 2017 [M²FCN] [Suggestive Annotation (SA)] [3D U-Net+ResNet] [Cascaded 3D U-Net] [DenseVoxNet] 2018 [QSA+QNT] [Attention U-Net] [RU-Net & R2U-Net] [VoxResNet] [UNet++] [H-DenseUNet] 2019 [DUNet] [NN-Fit]
2020: [MultiResUNet] [UNet 3+] [VGGNet for COVID-19] [Dense-Gated U-Net (DGNet)]

1.10. Face Recognition

2005 [Chopra CVPR’05] 2014 [DeepFace] [DeepID2] [CASIANet] 2015 [FaceNet] 2016 [N-pair-mc Loss]

1.11. Human Pose Estimation

2014–2015 [DeepPose] [Tompson NIPS’14] [Tompson CVPR’15] 2016 [CPM] [FCGN] [IEF] [DeepCut & DeeperCut] [Newell ECCV’16 & Newell POCV’16] 2017 [G-RMI] [CMUPose & OpenPose] [Mask R-CNN]

1.12. Video Classification / Action Recognition

2014 [Deep Video] [Two-Stream ConvNet] 2015 [DevNet] [C3D] 2016 [TSN] 2017 [Temporal Modeling Approaches] [4 Temporal Modeling Approaches] [P3D] [I3D] 2018 [NL: Non-Local Neural Networks]

1.13. Data Visualization

2002 [SNE] 2006 [Autoencoder] [DrLIM] 2007 [UNI-SNE] 2008 [t-SNE]

2. Image Generation Related

2.1. Generative Adversarial Network (GAN)

Image Synthesis: 2014 [GAN] [CGAN] 2015 [LAPGAN] 2016 [AAE] [DCGAN] [CoGAN] [VAE-GAN] [InfoGAN] 2017 [SimGAN] [BiGAN] [ALI] [LSGAN] [EBGAN] 2019 [SAGAN]
Image-to-image Translation: 2017 [Pix2Pix] [UNIT] [CycleGAN] 2018 [MUNIT]
Super Resolution: 2017 [SRGAN & SRResNet] [EnhanceNet] 2018 [ESRGAN]
Blur Detection: 2019 [DMENet]
Camera Tampering Detection: 2019 [Mantini’s VISAPP’19]
Video Coding: 2018
[VC-LAPGAN] 2020 [Zhu TMM’20] 2021 [Zhong ELECGJ’21]

2.2. Style Transfer

2016 [Image Style Transfer]

3. Image Reconstruction Related

3.1. Single Image Super Resolution (SISR)

2014–2016 [SRCNN] 2016 [FSRCNN] [VDSR] [ESPCN] [RED-Net] [DRCN]
2017 [DnCNN] [DRRN] [LapSRN & MS-LapSRN] [MemNet] [IRCNN] [WDRN / WavResNet] [SRDenseNet] [SRGAN & SRResNet] [SelNet] [CNF] [BT-SRN] [EDSR & MDSR] [EnhanceNet] 2018 [MWCNN] [MDesNet] [RDN] [SRMD & SRMDNF] [DBPN & D-DBPN] [RCAN] [ESRGAN] [CARN] [IDN] [ZSSR] [MSRN] [Image Transformer] 2019 [SR+STN] [IDBP-CNN-IA] [SRFBN] [OISR] 2020 [PRLSR] [CSFN & CSFN-M]

3.2. Image Restoration

2016 [RED-Net] 2017 [DnCNN] [MemNet] [IRCNN] [WDRN / WavResNet] 2018 [MWCNN] 2019 [IDBP-CNN-IA]

3.3. Video Super Resolution (VSR)

2017 [STMC / VESPCN] 2018 [VSR-DUF / DUF] 2019 [EDVR]

3.4. Video Frame Interpolation / Extrapolation

2016 [Mathieu ICLR’16] 2017 [AdaConv] [SepConv] 2020 [DSepConv] 2021 [SepConv++]

4. Natural Language Processing (NLP)

4.1. Language/Sequence Model

2007 [Bengio TNN’07] 2013 [Word2Vec] [NCE] [Negative Sampling] 2014 [GloVe] [GRU] [Doc2Vec] 2015 [Skip-Thought] 2016 [GCNN/GLU] [context2vec] [Jozefowicz arXiv’16] [LSTM-Char-CNN] [Layer Norm, LN] 2017 [TagLM] [CoVe] [MoE] [fastText] 2018 [GLUE] [T-DMCA] [GPT, GPT-1] [ELMo] 2019 [T64] [Transformer-XL] [BERT] [RoBERTa] [GPT-2] [DistilBERT] [MT-DNN] [Sparse Transformer] [SuperGLUE] 2020 [ALBERT] [GPT-3] [T5] [Pre-LN Transformer]

4.2. Machine Translation

2014 [Seq2Seq] [RNN Encoder-Decoder] 2015 [Attention Decoder/RNNSearch] 2016 [GNMT] [ByteNet] [Deep-ED & Deep-Att] 2017 [ConvS2S] [Transformer] [MoE] [GMNMT] [CoVe] 2018 [Shaw NAACL’18] 2019 [AdaNorm] [GPT-2] [Pre-Norm Transformer] 2020 [Batch Augment, BA] [GPT-3] [T5] [Pre-LN Transformer] 2021 [ResMLP]

5. Vision-Language

5.1. Image Captioning

2015 [m-RNN] [R-CNN+BRNN] [Show and Tell/NIC] [Show, Attend and Tell]

PhD, Researcher. I share what I've learnt and done. :) My LinkedIn: https://www.linkedin.com/in/sh-tsang/, My Paper Reading List: https://bit.ly/33TDhxG

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Sik-Ho Tsang

Sik-Ho Tsang

PhD, Researcher. I share what I've learnt and done. :) My LinkedIn: https://www.linkedin.com/in/sh-tsang/, My Paper Reading List: https://bit.ly/33TDhxG

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