A curated list of papers on disentangled representation learning inspired by https://github.com/sootlasten/disentangled-representation-papers and https://github.com/matthewvowels1/Awesome-VAEs.
Since the original curated list(sootlasten/disentangled-representation-papers) seems to be stopped now, and I would like to add some summarization using GitHub issues, I decided not to fork the repository but make a new curated list.
To respect the original repository, I've added a tag([copied]) in front of the name of the paper which was originally listed in sootlasten/disentangled-representation-papers. I also use the star(☆) and ? notation to show the importance of the paper following the original repository. The judges of the original repository are remained, but ? may be replaced by my judge.? notation show that I haven't fully read the paper, and ☆ indicates the importance/quality of each paper (the larger the number of the stars, the better the importance/quality is).
- ? On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (Jun, Gondal et. al.) [paper]
- ? On the Fairness of Disentangled Representations (May, Locatello et. al.) [paper]
- ☆☆☆ Variational Autoencoders and Nonlinear ICA: A Unifying Framework (Jul, Khemakhem et. al.) [paper]
- ? Explicitly disentangling image content from translation and rotation with spatial-VAE (Sep, Bepler et. al.) [paper] [code]
- ?
[copied]Are Disentangled Representations Helpful for Abstract Visual Reasoning? (May, Steenkiste et. al.)[paper]
- ? Discovering Interpretable Representations for Both Deep Generative and Discriminative Models (Jul, Adel et al.) [paper]
- ?
[copied]Hyperprior Induced Unsupervised Disentanglement of Latent Representations (Jan, Ansari and Soh) [paper] - ?
[copied]A Spectral Regularizer for Unsupervised Disentanglement (Dec, Ramesh et. al.) [paper] - ?
[copied]Disentangling Disentanglement (Dec, Mathieu et. al.) [paper] - ?
[copied]Recent Advances in Autoencoder-Based Representation Learning (Dec, Tschannen et. al.) [paper] - ?
[copied]Visual Object Networks: Image Generation with Disentangled 3D Representation (Dec, Zhu et. al.) [paper] - ?
[copied]Towards a Definition of Disentangled Representations (Dec, Higgins et. al.) [paper] - ☆☆☆
[copied]Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (Dec, Locatello et. al.) [paper] [code] - ?
[copied]Learning Deep Representations by Mutual Information Estimation and Maximization (Aug, Hjelm et. al.) [paper] - ☆☆
[copied]Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (Aug, Achille et. al.) [paper] - ?
[copied]Learning to Decompose and Disentangle Representations for Video Prediction (Hsieh et. al.) [paper] - ?
[copied]Insights on Representational Similarity in Neural Networks with Canonical Correlation (Jun, Morcos et. al.) [paper] - ☆☆
[copied]Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (Jun, Kosiorek et. al.) [paper] - ☆☆☆
[copied]Neural Scene Representation and Rendering (Jun, Eslami et. al.) [paper] - ?
[copied]Image-to-image translation for cross-domain disentanglement (May, Gonzalez-Garcia et. al.) [paper] - ☆
[copied]Learning Disentangled Joint Continuous and Discrete Representations (May, Dupont) [paper] [code] [summary-slide] [thread] - ?
[copied]DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis (Apr, Bem et. al.) [paper] - ?
[copied]Structured Disentangled Representations (Apr, Esmaeili et. al.) [paper] - ☆☆
[copied]Understanding disentangling in β-VAE (Apr, Burgess et. al.) [paper] - ?
[copied]On the importance of single directions for generalization (Mar, Morcos et. al.) [paper] - ☆☆
[copied]Unsupervised Representation Learning by Predicting Image Rotations (Mar, Gidaris et. al.) [paper] - ?
[copied]Disentangled Sequential Autoencoder (Mar, Li & Mandt) [paper] - ☆☆☆
[copied]Isolating Sources of Disentanglement in Variational Autoencoders (Mar, Chen et. al.) [paper] [code] - ☆☆
[copied]Disentangling by Factorising (Feb, Kim & Mnih) [paper] - ☆☆
[copied]Disentangling the Independently Controllable Factors of Variation by Interacting with the World (Feb, Bengio's group) [paper] - ?
[copied]On the Latent Space of Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [paper] - ?
[copied]Auto-Encoding Total Correlation Explanation (Feb, Gao et. al.) [paper] - ?
[copied]Fixing a Broken ELBO (Feb, Alemi et. al.) [paper] - ☆
[copied]Learning Disentangled Representations with Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [paper] - ?
[copied]Rethinking Style and Content Disentanglement in Variational Autoencoders (Feb, Shu et. al.) [paper] - ?
[copied]A Framework for the Quantitative Evaluation of Disentangled Representations (Feb, Eastwood & Williams) [paper]
- ?
[copied]The β-VAE's Implicit Prior (Dec, Hoffman et. al.) [paper] - ☆☆
[copied]The Multi-Entity Variational Autoencoder (Dec, Nash et. al.) [paper] - ?
[copied]Learning Independent Causal Mechanisms (Dec, Parascandolo et. al.) [paper] - ?
[copied]Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (Nov, Kumar et. al.) [paper] - ☆
[copied]Neural Discrete Representation Learning (Nov, Oord et. al.) [paper] - ?
[copied]Disentangled Representations via Synergy Minimization (Oct, Steeg et. al.) [paper] - ?
[copied]Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data (Sep, Hsu et. al.) [paper] [code] - ☆
[copied]Experiments on the Consciousness Prior (Sep, Bengio & Fedus) [paper] - ☆☆
[copied]The Consciousness Prior (Sep, Bengio) [paper] - ?
[copied]Disentangling Motion, Foreground and Background Features in Videos (Jul, Lin. et. al.) [paper] - ☆
[copied]SCAN: Learning Hierarchical Compositional Visual Concepts (Jul, Higgins. et. al.) [paper] - ☆☆☆
[copied]DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (Jul, Higgins et. al.) [paper] - ☆☆
[copied]Unsupervised Learning via Total Correlation Explanation (Jun, Ver Steeg) [paper] [code] - ?
[copied]PixelGAN Autoencoders (Jun, Makhzani & Frey) [paper] - ?
[copied]Emergence of Invariance and Disentanglement in Deep Representations (Jun, Achille & Soatto) [paper] - ☆☆
[copied]A Simple Neural Network Module for Relational Reasoning (Jun, Santoro et. al.) [paper] - ?
[copied]Learning Disentangled Representations with Semi-Supervised Deep Generative Models (Jun, Siddharth, et al.) [paper] - ?
[copied]Unsupervised Learning of Disentangled Representations from Video (May, Denton & Birodkar) [paper] - ? Multi-Level Variational Autoencoder: Learning Disentangled representations from Grouped Observations (May, Bouchacourt et. al.) [paper] [code]
- ☆☆
[copied]Deep Variational Information Bottleneck (Dec, Alemi et. al.) [paper] - ☆☆☆
[copied]β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (Nov, Higgins et. al.) [paper] [code] - ?
[copied]Disentangling factors of variation in deep representations using adversarial training (Nov, Mathieu et. al.) [paper] - ☆☆
[copied]Information Dropout: Learning Optimal Representations Through Noisy Computation (Nov, Achille & Soatto) [paper] - ☆☆
[copied]InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (Jun, Chen et. al.) [paper] - ☆☆☆
[copied]Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (Mar, Eslami et. al.) [paper] - ☆☆☆
[copied]Building Machines That Learn and Think Like People (Apr, Lake et. al.) [paper] - ☆
[copied]Understanding Visual Concepts with Continuation Learning (Feb, Whitney et. al.) [paper] - ?
[copied]Disentangled Representations in Neural Models (Feb, Whitney) [paper]
- ☆☆
[copied]Deep Convolutional Inverse Graphics Network (2015, Kulkarni et. al.) [paper] - ?
[copied]Learning to Disentangle Factors of Variation with Manifold Interaction (2014, Reed et. al.) [paper] - ☆☆☆
[copied]Representation Learning: A Review and New Perspectives (2013, Bengio et. al.) [paper] - ?
[copied]Disentangling Factors of Variation via Generative Entangling (2012, Desjardinis et. al.) [paper] - ☆☆☆
[copied]Transforming Auto-encoders (2011, Hinton et. al.) [paper] - ☆☆
[copied]Learning Factorial Codes By Predictability Minimization (1992, Schmidhuber) [paper] - ☆☆☆
[copied]Self-Organization in a Perceptual Network (1988, Linsker) [paper] - ☆☆ Semi-supervised Learning with Deep Generative Models (2014, Kingma et. al.) [paper] [code]