Learning useful representations with the unsupervised or weakly supervised methods is a key challenge in artificial intelligence. The representation learning (RL) is helpful for most specific tasks like classification, recognition, detection, image editing, image retrieval, et al. in computer vision area. RL is mainly appeared in the learning good representations for down-stream task, disentangled representation/attributes, VAE, GAN, Flow-based model, image-translation, deep clustering papers. This repo mainly focuses on the lasted development in the RL area.

I hope this repo helps both you and me. If you find some mistakes, other novel or interesting works related to representation learning, please don't hesitate to issue or pull request.

• 1. Related Metrics
• 2. Related Survey
• 3. General Representation Learning (self-supervised, contrastive learning, ...)
• 4. Disentangled Representation
• 5. Disassembling Object Representation
• 6. VAE-based Method
• 7. GAN-based Method
• 8. Flow-based Method
• 9. Image Translation
• 10. Deep Clustering
• 11. Other Related Method
• 12. Resources

• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology, arXiv2020 [code]
• Theory and Evaluation Metrics for Learning Disentangled Representations, arXiv2019 [code]
• A framework for the quantitative evaluation of disentangled representations, ICLR2018 [code]

• Representation learning: A review and new perspectives, PAMI2013, Yoshua Bengio
• Recent Advances in Autoencoder-Based Representation Learning, arXiv2018

include self-supervised, contrastive learning, ...

• Masked Autoencoders Are Scalable Vision Learners, arXiv2021
  codes-pytorch

• A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning, CVPR2021
  codes

• An Empirical Study of Training Self-Supervised Visual Transformers, arXiv2021, MoCov3[transformer]

• Barlow Twins-Self-Supervised Learning via Redundancy Reduction, arXiv2021, Twins
  codes-pytorch

• Exploring Simple Siamese Representation Learning, arXiv2020, SimSiam
  codes-pytorch

• Bootstrap your own latent-A new approach to self-supervised Learning, arXiv2020, BYOL
  codes-pytorch

• Parametric Instance Classification for Unsupervised Visual Feature Learning, arXiv2020, PIC

• A Universal Representation Transformer Layer for Few-Shot Image Classification, arXiv2020
  codes-tensorflow

• Self-supervised Learning Generative or Contrastive, arXiv2020

• Gradients as Features for Deep Representation Learning, ICLR2020
  codes-pytorch

• Big Self-Supervised Models are Strong Semi-Supervised Learners, arXiv2020, SimCLR-v2
  codes-tensorflow

• A simple framework for contrastive learning of visual representations, arXiv2020, SimCLR-v1
  codes-pytorch

• Improved Baselines with Momentum Contrastive Learning, arXiv2020, MoCo-v2
  codes-pytorch-unofficial

• Momentum Contrast for Unsupervised Visual Representation Learning, CVPR2020, MoCo-v1
  codes-pytorch

• Self-supervised learning of pretext-invariant representations, CVPR2020, PIRL
  codes-pytorch-unofficial

• Prototypical Contrastive Learning of Unsupervised Representations, arXiv2020

• Supervised Contrastive Learning, arXiv2020
  codes-pytorch

• Contrastive representation distillation. ICLR2020
  codes-pytorch

• Learning deep representations by mutual information estimation and maximization, ICLR2019
  codes-pytorch

• Revisiting self-supervised visual representation learning, CVPR2019

• Representation learning with contrastive predictive coding, arXiv2018
  codes-keras

• Unsupervised feature learning via non-parametric instance discrimination, CVPR2018
  codes-pytorch

• Contrastive learning of emoji-based representations for resource-poor languages

• Order-Embeddings of Images and Language, ICLR2016
  codes-theano

• Unsupervised Visual Representation Learning by Context Prediction, ICCV2015
  code-caffe

• ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping, arXiv2020
  codes

• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations, ICML2019

• Emerging disentanglement in auto-encoder based unsupervised image content transfer, ICLR2019
  codes-pytorch

• Are Disentangled Representations Helpful for Abstract Visual Reasoning?, NeurIPS2019

• Hyperprior induced unsupervised disentanglement of latent representations, AAAI2019
  codes-tensorflow

• Learning disentangled joint continuous and discrete representations, NeurIPS2018

• Unsupervised representation learning by predicting image rotations, ICLR2018
  codes-pytorch

• A Two-Step Disentanglement Method, CVPR2018
  codes-keras

• Disentangling by factorising, ICML2018
  codes-pytorch

• Isolating sources of disentanglement in variational autoencoders, NeurIPS2018
  codes-pytorch

• Life-long disentangled representation learning with cross-domain latent homologies, NeurIPS2018

• A Spectral Regularizer for Unsupervised Disentanglement, arXiv2018

• Visual object networks: Image generation with disentangled 3D representations, NeurIPS2018

• Understanding disentangling in beta-VAE, arXiv2018

• Disentangling the independently controllable factors of variation by interacting with the world, arXiv2018

• Dual swap disentangling, NeurIPS2018

• Unsupervised learning of disentangled representations from video, NeurIPS2017
  codes-pytorch

• One-sample Guided Object Representation Disassembling, Neurips2020
  codes-tensorflow

• Disassembling Object Representations without Labels, arXiv2020

• Learning to Manipulate Individual Objects in an Image, CVPR2020

• Monet: Unsupervised scene decomposition and representation, arXiv2019
  codes-pytorch

• Multi-object representation learning with iterative variational inference, ICML2019
  codes-pytorch

• Object Discovery with a Copy-Pasting GAN, arXiv2019
  codes-tensorflow

• GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, ICLR2019
  codes-pytorch

• Stacked capsule autoencoders, NeurIPS2019
  codes-pytorch

• LAVAE: Disentangling Location and Appearance, arXiv2019

• Unsupervised object segmentation by redrawing, NeurIPS2019
  codes-pytorch

• Relational neural expectation maximization: Unsupervised discovery of objects and their interactions, ICLR2018
  codes-tensorflow

• Attend, infer, repeat: Fast scene understanding with generative models, NeurIPS2016
  codes-pytorch

• Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes, arXiv2019

• Disentangling Disentanglement in Variational Autoencoders, ICML2019

• Disentangled Sequential Autoencoder, ICML2018
  codes-pytorch

• beta-VAE Learning Basic Visual Concepts with a Constrained Variational Framework, ICLR2017
  codes-pytorch

• Auto-Encoding Variational Bayes, ICLR2014
  codes-keras, codes-pytorch

• Transformation GAN for Unsupervised Image Synthesis and Representation Learning, CVPR2020

• Large scale adversarial representation learning, NeurIPS2019
  codes-tensorflow

• PixelGAN autoencoders, NeurIPS2017

• Adversarial feature learning, ICLR2017
  codes-theano

• Infogan: Interpretable representation learning by information maximizing generative adversarial nets, NeurIPS2016
  codes-tensorflow

• Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv2015
  codes-tensorflow

• Flow++: Improving flow-based generative models with variational dequantization and architecture design, ICML2019

• Glow Generative flow with invertible 1x1 convolutions, NeurIPS2018
  codes-tensorflow

• Masked autoregressive flow for density estimation, NeurIPS2017
  codes-tensorflow-unofficial

• Improved variational inference with inverse autoregressive flow, NeurIPS2016
  codes-theano

• High Resolution Face Age Editing, arXiv2020
  codes-pytorch

• Rethinking the Truly Unsupervised Image-to-Image Translation, arXiv2020
  codes-pytorch

• Diverse Image-to-Image Translation via Disentangled Representations, ECCV2018
  codes-tensorflow

• Image-to-image translation for cross-domain disentanglement, NeurIPS2018
  codes-tensorflow

• Invariant information clustering for unsupervised image classification and segmentation, ICCV2019
  codes-pytorch
• Deep Spectral Clustering using Dual Autoencoder Network, CVPR2019
  codes-tensorflow

• Deep Clustering for Unsupervised Learning of Visual Features, ECCV2018
  codes-pytorch

• Clustering with Deep Learning: Taxonomy and New Methods, arXiv2018
  codes-theano

• Contrastive Deep Supervision, ECCV2022
  codes-pytorch

• disentangled-representation-papers, update 13 months ago
• Self-supervised learning and computer vision, 13 Jan 2020
• Self-Supervised Representation Learning, 10 Nov 2019