README
Paper Reading List
useful tools
experiments
The experiments in this repository have been conducted in Colab and also on my desktop.
We also use VSCode's Remote-Development function and Docker to construct the environment for experiments in the paper.
In this repository, we are experimenting on a Docker container running under a remote GPU environment, and accessing the remote terminal from a local VSCode. If you want to run your experiments in this environment, please refer to the tips/vscode/RemoteDevelopment folder.
TOC
- Paper Reading List
Generative Models
Image Generation
- [arXiv:1710.10196] Progressive Growing of GANs for Improved Quality, Stability, and Variation
- [arXiv:1805.08318] Self-Attention Generative Adversarial Networks
- [arXiv:1809.11096] Large Scale GAN Training for High Fidelity Natural Image Synthesis
- [arXiv:1905.01164] SinGAN: Learning a Generative Model from a Single Natural Image
- [arXiv:1912.11035] CNN-generated images are surprisingly easy to spot... for now
- [arXiv:2002.10964] Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
- [arXiv:2002.12655] A U-Net Based Discriminator for Generative Adversarial Networks
- [arXiv:2004.02088] Feature Quantization Improves GAN Training
- [arXiv:2003.02567] GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
- [arXiv:2004.03355] Inclusive GAN: Improving Data and Minority Coverage in Generative Models
- [arXiv:2004.05472] Autoencoding Generative Adversarial Networks
- [arXiv:2006.12681] Contrastive Generative Adversarial Networks
- [arXiv:2006.14567] Taming GANs with Lookahead
- [arXiv:2007.06600] Closed-Form Factorization of Latent Semantics in GANs
Few-Shot Image Generation
Self Supervised
- [arXiv:1810.01365] On Self Modulation for Generative Adversarial Networks
- [arXiv:1811.11212] Self-Supervised GANs via Auxiliary Rotation Loss
- [arXiv:2006.10728] Diverse Image Generation via Self-Conditioned GANs
- [arXiv:2010.09893] LT-GAN: Self-Supervised GAN with Latent Transformation Detection
Image-to-Image Translation
- [arXiv:1611.07004] Image-to-Image Translation with Conditional Adversarial Networks
- [arXiv:1711.09020] StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
- [arXiv:1711.11585] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- [arXiv:1812.10889] InstaGAN: Instance-aware Image-to-Image Translation
- [arXiv:1907.04312] Positional Normalization
- [arXiv:1910.05253] Adversarial Colorization Of Icons Based On Structure And Color Conditions
- [arXiv:2002.05638] GANILLA: Generative Adversarial Networks for Image to Illustration Translation
- [arXiv:2003.00187] Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation
- [arXiv:2003.02683] Image Generation from Freehand Scene Sketches
- [arXiv:2003.00273] Reusing Discriminators for Encoding Towards Unsupervised Image-to-Image Translation
- [arXiv:2003.04858] Unpaired Image-to-Image Translation using Adversarial Consistency Loss
- [arXiv:2003.07101] Synthesizing human-like sketches from natural images using a conditional convolutional decoder
- [arXiv:2007.05471] Geometric Style Transfer
Text-to-Image Translation
- [arXiv:1612.03242] StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
- [arXiv:1802.09178] Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network
- [arXiv:1904.01480] Semantics Disentangling for Text-to-Image Generation
- [arXiv:2003.12137] Cycle Text-To-Image GAN with BERT
- [arXiv:2004.11437] Efficient Neural Architecture for Text-to-Image Synthesis
- [arXiv:2005.12444] SegAttnGAN: Text to Image Generation with Segmentation Attention
- [arXiv:2005.13192] TIME: Text and Image Mutual-Translation Adversarial Networks
- [arXiv:2008.05865] DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis
- [arXiv:2008.08976] Improving Text to Image Generation using Mode-seeking Function
Speech-to-Image Translation
Super-Resolution
- [arXiv:2003.02365] Creating High Resolution Images with a Latent Adversarial Generator
- [arXiv:2004.00448] Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Image Editting
Denoising
Disentanglement
Regularization
- [arXiv:1903.05628] Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
- [arXiv:1904.12848] Unsupervised Data Augmentation for Consistency Training
- [arXiv:1910.12027] Consistency Regularization for Generative Adversarial Networks
- [arXiv:2002.04724] Improved Consistency Regularization for GAN
- [arXiv:2006.02595] Image Augmentations for GAN Training
- [arXiv:2006.05338] Towards Good Practices for Data Augmentation in GAN Training
- [arXiv:2006.06676] Training Generative Adversarial Networks with Limited Data
- [arXiv:2006.10738] Differentiable Augmentation for Data-Efficient GAN Training
Compression
- [arXiv:2003.08936] GAN Compression: Efficient Architectures for Interactive Conditional GANs
- [arXiv:2006.08198] AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
- [arXiv:2009.13829] TinyGAN: Distilling BigGAN for Conditional Image Generation
Survey
Variational AutoEncoder
Computer Vision
Learning Strategy
- [arXiv:1901.04596] AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
- [arXiv:1907.08610] Lookahead Optimizer: k steps forward, 1 step back
- [arXiv:1911.05722] Momentum Contrast for Unsupervised Visual Representation Learning
- [arXiv:1911.09665] Adversarial Examples Improve Image Recognition
- [arXiv:2004.11362] Supervised Contrastive Learning
- [arXiv:2003.00152] Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
- [arXiv:2010.05981] Shape-Texture Debiased Neural Network Training
Architecture
- [arXiv:1811.12231] ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
- [arXiv:1906.05909] Stand-Alone Self-Attention in Vision Models
- [arXiv:1911.08432] Defective Convolutional Layers Learn Robust CNNs
- [arXiv:2003.01367] Curriculum By Texture
- [arXiv:2004.13587] Do We Need Fully Connected Output Layers in Convolutional Networks?
- [arXiv:2004.13621] Exploring Self-attention for Image Recognition
- [arXiv:2006.03677] Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Architecture Search
Normalization
Data Augmentation
- [arXiv:1905.04899] CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
- [arXiv:1909.09148] Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
- [arXiv:1909.12220] Implicit Semantic Data Augmentation for Deep Networks
- [arXiv:2002.11102] On Feature Normalization and Data Augmentation
Object Detection
- [arXiv:2003.05176] Equalization Loss for Long-Tailed Object Recognition
- [arXiv:2004.08955] ResNeSt: Split-Attention Networks
Self-Supervised Learning
- [arXiv:2002.05709] A Simple Framework for Contrastive Learning of Visual Representations
- [arXiv:2003.04297] Improved Baselines with Momentum Contrastive Learning
- [arXiv:2006.07733] Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Anomaly Detection
Natural Language Processing
Transformers
- [arXiv:2003.07845] Rethinking Batch Normalization in Transformers
- [arXiv:2002.11794] Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
- [arXiv:2004.02984] MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
- [arXiv:2004.11886] Lite Transformer with Long-Short Range Attention
- [arXiv:2005.00743] Synthesizer: Rethinking Self-Attention in Transformer Models
Data Augmentation
Survey
Tabular
Concept Shift
Learning Resources
Significant stargazers
Omry Yadan
608 followers · starred May 2020
Similar repos
README
Paper Reading List
useful tools
experiments
The experiments in this repository have been conducted in Colab and also on my desktop.
We also use VSCode's Remote-Development function and Docker to construct the environment for experiments in the paper.
In this repository, we are experimenting on a Docker container running under a remote GPU environment, and accessing the remote terminal from a local VSCode. If you want to run your experiments in this environment, please refer to the tips/vscode/RemoteDevelopment folder.
TOC
- Paper Reading List
Generative Models
Image Generation
- [arXiv:1710.10196] Progressive Growing of GANs for Improved Quality, Stability, and Variation
- [arXiv:1805.08318] Self-Attention Generative Adversarial Networks
- [arXiv:1809.11096] Large Scale GAN Training for High Fidelity Natural Image Synthesis
- [arXiv:1905.01164] SinGAN: Learning a Generative Model from a Single Natural Image
- [arXiv:1912.11035] CNN-generated images are surprisingly easy to spot... for now
- [arXiv:2002.10964] Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
- [arXiv:2002.12655] A U-Net Based Discriminator for Generative Adversarial Networks
- [arXiv:2004.02088] Feature Quantization Improves GAN Training
- [arXiv:2003.02567] GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
- [arXiv:2004.03355] Inclusive GAN: Improving Data and Minority Coverage in Generative Models
- [arXiv:2004.05472] Autoencoding Generative Adversarial Networks
- [arXiv:2006.12681] Contrastive Generative Adversarial Networks
- [arXiv:2006.14567] Taming GANs with Lookahead
- [arXiv:2007.06600] Closed-Form Factorization of Latent Semantics in GANs
Few-Shot Image Generation
Self Supervised
- [arXiv:1810.01365] On Self Modulation for Generative Adversarial Networks
- [arXiv:1811.11212] Self-Supervised GANs via Auxiliary Rotation Loss
- [arXiv:2006.10728] Diverse Image Generation via Self-Conditioned GANs
- [arXiv:2010.09893] LT-GAN: Self-Supervised GAN with Latent Transformation Detection
Image-to-Image Translation
- [arXiv:1611.07004] Image-to-Image Translation with Conditional Adversarial Networks
- [arXiv:1711.09020] StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
- [arXiv:1711.11585] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- [arXiv:1812.10889] InstaGAN: Instance-aware Image-to-Image Translation
- [arXiv:1907.04312] Positional Normalization
- [arXiv:1910.05253] Adversarial Colorization Of Icons Based On Structure And Color Conditions
- [arXiv:2002.05638] GANILLA: Generative Adversarial Networks for Image to Illustration Translation
- [arXiv:2003.00187] Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation
- [arXiv:2003.02683] Image Generation from Freehand Scene Sketches
- [arXiv:2003.00273] Reusing Discriminators for Encoding Towards Unsupervised Image-to-Image Translation
- [arXiv:2003.04858] Unpaired Image-to-Image Translation using Adversarial Consistency Loss
- [arXiv:2003.07101] Synthesizing human-like sketches from natural images using a conditional convolutional decoder
- [arXiv:2007.05471] Geometric Style Transfer
Text-to-Image Translation
- [arXiv:1612.03242] StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
- [arXiv:1802.09178] Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network
- [arXiv:1904.01480] Semantics Disentangling for Text-to-Image Generation
- [arXiv:2003.12137] Cycle Text-To-Image GAN with BERT
- [arXiv:2004.11437] Efficient Neural Architecture for Text-to-Image Synthesis
- [arXiv:2005.12444] SegAttnGAN: Text to Image Generation with Segmentation Attention
- [arXiv:2005.13192] TIME: Text and Image Mutual-Translation Adversarial Networks
- [arXiv:2008.05865] DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis
- [arXiv:2008.08976] Improving Text to Image Generation using Mode-seeking Function
Speech-to-Image Translation
Super-Resolution
- [arXiv:2003.02365] Creating High Resolution Images with a Latent Adversarial Generator
- [arXiv:2004.00448] Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Image Editting
Denoising
Disentanglement
Regularization
- [arXiv:1903.05628] Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
- [arXiv:1904.12848] Unsupervised Data Augmentation for Consistency Training
- [arXiv:1910.12027] Consistency Regularization for Generative Adversarial Networks
- [arXiv:2002.04724] Improved Consistency Regularization for GAN
- [arXiv:2006.02595] Image Augmentations for GAN Training
- [arXiv:2006.05338] Towards Good Practices for Data Augmentation in GAN Training
- [arXiv:2006.06676] Training Generative Adversarial Networks with Limited Data
- [arXiv:2006.10738] Differentiable Augmentation for Data-Efficient GAN Training
Compression
- [arXiv:2003.08936] GAN Compression: Efficient Architectures for Interactive Conditional GANs
- [arXiv:2006.08198] AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
- [arXiv:2009.13829] TinyGAN: Distilling BigGAN for Conditional Image Generation
Survey
Variational AutoEncoder
Computer Vision
Learning Strategy
- [arXiv:1901.04596] AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
- [arXiv:1907.08610] Lookahead Optimizer: k steps forward, 1 step back
- [arXiv:1911.05722] Momentum Contrast for Unsupervised Visual Representation Learning
- [arXiv:1911.09665] Adversarial Examples Improve Image Recognition
- [arXiv:2004.11362] Supervised Contrastive Learning
- [arXiv:2003.00152] Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
- [arXiv:2010.05981] Shape-Texture Debiased Neural Network Training
Architecture
- [arXiv:1811.12231] ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
- [arXiv:1906.05909] Stand-Alone Self-Attention in Vision Models
- [arXiv:1911.08432] Defective Convolutional Layers Learn Robust CNNs
- [arXiv:2003.01367] Curriculum By Texture
- [arXiv:2004.13587] Do We Need Fully Connected Output Layers in Convolutional Networks?
- [arXiv:2004.13621] Exploring Self-attention for Image Recognition
- [arXiv:2006.03677] Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Architecture Search
Normalization
Data Augmentation
- [arXiv:1905.04899] CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
- [arXiv:1909.09148] Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
- [arXiv:1909.12220] Implicit Semantic Data Augmentation for Deep Networks
- [arXiv:2002.11102] On Feature Normalization and Data Augmentation
Object Detection
- [arXiv:2003.05176] Equalization Loss for Long-Tailed Object Recognition
- [arXiv:2004.08955] ResNeSt: Split-Attention Networks
Self-Supervised Learning
- [arXiv:2002.05709] A Simple Framework for Contrastive Learning of Visual Representations
- [arXiv:2003.04297] Improved Baselines with Momentum Contrastive Learning
- [arXiv:2006.07733] Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Anomaly Detection
Natural Language Processing
Transformers
- [arXiv:2003.07845] Rethinking Batch Normalization in Transformers
- [arXiv:2002.11794] Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
- [arXiv:2004.02984] MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
- [arXiv:2004.11886] Lite Transformer with Long-Short Range Attention
- [arXiv:2005.00743] Synthesizer: Rethinking Self-Attention in Transformer Models
Data Augmentation
Survey
Tabular
Concept Shift
Learning Resources
Similar repos
Significant stargazers
Omry Yadan
608 followers · starred May 2020