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https://github.com/sootlasten/disentangled-representation-papers

A curated list of research papers related to learning disentangled representations
https://github.com/sootlasten/disentangled-representation-papers

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A curated list of research papers related to learning disentangled representations

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README 

        
This is a curated list of papers on disentangled (and an occasional "conventional") representation learning. Within each year, the papers are ordered from newest to oldest. I've scored the importance/quality of each paper (in my own personal opinion) on a scale of 1 to 3, as indicated by the number of stars in front of each entry in the list. If stars are replaced by a question mark, then it represents a paper I haven't fully read yet, in which case I'm unable to judge its quality.



### 2019



* ? Are Disentangled Representations Helpful for Abstract Visual Reasoning? (May, Steenkiste et. al.) [[paper]](https://arxiv.org/abs/1905.12506)



## 2018 



* ? Hyperprior Induced Unsupervised Disentanglement of Latent Representations (Jan, Ansari and Soh) [[paper]](https://arxiv.org/abs/1809.04497)

* ? A Spectral Regularizer for Unsupervised Disentanglement (Dec, Ramesh et. al.) [[paper]](https://arxiv.org/abs/1812.01161v1)

* ? Disentangling Disentanglement  (Dec, Mathieu et. al.) [[paper]](https://arxiv.org/abs/1812.02833v1)

* ? Recent Advances in Autoencoder-Based Representation Learning (Dec, Tschannen et. al.) [[paper]](http://bayesiandeeplearning.org/2018/papers/151.pdf?fbclid=IwAR0AKPuAsCFFsTCJ52o6-BkJebR9UuURnesksd1wf5QfLvuU2LBetc7moKc)

* ? Visual Object Networks: Image Generation with Disentangled 3D Representation (Dec, Zhu et. al.) [[paper]](https://arxiv.org/abs/1812.02725v1)

* ? Towards a Definition of Disentangled Representations (Dec, Higgins et. al.) [[paper]](https://arxiv.org/pdf/1812.02230v1.pdf)

* ? Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (Dec, Locatello et. al.) [[paper]](https://arxiv.org/abs/1811.12359v1)

* ? Learning Deep Representations by Mutual Information Estimation and Maximization (Aug, Hjelm et. al.) [[paper]](https://arxiv.org/abs/1808.06670)

* ** Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (Aug, Achille et. al.) [[paper]](https://arxiv.org/abs/1808.06508)

* ? Learning to Decompose and Disentangle Representations for Video Prediction (Hsieh et. al.) [[paper]](https://arxiv.org/abs/1806.04166)

* ? Insights on Representational Similarity in Neural Networks with Canonical Correlation (Jun, Morcos et. al.) [[paper]](https://arxiv.org/abs/1806.05759)

* ** Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (Jun, Kosiorek et. al.) [[paper]](https://arxiv.org/abs/1806.01794)

* __***__ Neural Scene Representation and Rendering (Jun, Eslami et. al.) [[paper]](https://deepmind.com/research/publications/neural-scene-representation-and-rendering/)

* ? Image-to-image translation for cross-domain disentanglement (May, Gonzalez-Garcia et. al.) [[paper]](https://arxiv.org/abs/1805.09730)

* \* Learning Disentangled Joint Continuous and Discrete Representations (May, Dupont) [[paper]](https://arxiv.org/abs/1804.00104) [[code]](https://github.com/Schlumberger/joint-vae)

* ? DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis (Apr, Bem et. al.) [[paper]](https://arxiv.org/abs/1804.06364)

* ? Structured Disentangled Representations (Apr, Esmaeili et. al.) [[paper]](https://arxiv.org/abs/1804.02086)

* ** Understanding disentangling in β-VAE (Apr, Burgess et. al.) [[paper]](https://arxiv.org/abs/1804.03599)

* ? On the importance of single directions for generalization (Mar, Morcos et. al.) [[paper]](https://arxiv.org/abs/1803.06959)

* ** Unsupervised Representation Learning by Predicting Image Rotations (Mar, Gidaris et. al.) [[paper]](https://arxiv.org/abs/1803.07728)

* ? Disentangled Sequential Autoencoder (Mar, Li & Mandt) [[paper]](https://arxiv.org/abs/1803.02991)

* __***__ Isolating Sources of Disentanglement in Variational Autoencoders (Mar, Chen et. al.) [[paper]](https://arxiv.org/abs/1802.04942v2) [[code]](https://github.com/rtqichen/beta-tcvae)

* ** Disentangling by Factorising (Feb, Kim & Mnih) [[paper]](https://arxiv.org/abs/1802.05983)

* ** Disentangling the Independently Controllable Factors of Variation by Interacting with the World (Feb, Bengio's group) [[paper]](https://arxiv.org/abs/1802.09484)

* ? On the Latent Space of Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [[paper]](https://arxiv.org/abs/1802.03761)

* ? Auto-Encoding Total Correlation Explanation (Feb, Gao et. al.) [[paper]](https://arxiv.org/abs/1802.05822v1)

* ? Fixing a Broken ELBO (Feb, Alemi et. al.) [[paper]](https://arxiv.org/abs/1711.00464)  

* \* Learning Disentangled Representations with Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [[paper]](https://openreview.net/forum?id=Hy79-UJPM)

* ? Rethinking Style and Content Disentanglement in Variational Autoencoders (Feb, Shu et. al.) [[paper]](https://openreview.net/forum?id=B1rQtwJDG)

* ? A Framework for the Quantitative Evaluation of Disentangled Representations (Feb, Eastwood & Williams) [[paper]](https://openreview.net/forum?id=By-7dz-AZ)



## 2017



* ? The β-VAE's Implicit Prior (Dec, Hoffman et. al.) [[paper]](http://bayesiandeeplearning.org/2017/papers/66.pdf)

* ** The Multi-Entity Variational Autoencoder (Dec, Nash et. al.) [[paper]](http://charlienash.github.io/assets/docs/mevae2017.pdf)

* ? Learning Independent Causal Mechanisms (Dec, Parascandolo et. al.) [[paper]](https://arxiv.org/abs/1712.00961)

* ? Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (Nov, Kumar et. al.) [[paper]](https://arxiv.org/abs/1711.00848)

* \* Neural Discrete Representation Learning (Nov, Oord et. al.) [[paper]](https://arxiv.org/abs/1711.00937v2)

* ? Disentangled Representations via Synergy Minimization (Oct, Steeg et. al.) [[paper]](https://arxiv.org/abs/1710.03839v1)

* ? Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data (Sep, Hsu et. al.) [[paper]](https://arxiv.org/abs/1709.07902) [[code]](https://github.com/wnhsu/ScalableFHVAE)

* \* Experiments on the Consciousness Prior (Sep, Bengio & Fedus) [[paper]](https://ai-on.org/pdf/bengio-consciousness-prior.pdf)

* ** The Consciousness Prior (Sep, Bengio) [[paper]](https://arxiv.org/abs/1709.08568)

* ? Disentangling Motion, Foreground and Background Features in Videos (Jul, Lin. et. al.) [[paper]](https://imatge-upc.github.io/unsupervised-2017-cvprw/)

* \* SCAN: Learning Hierarchical Compositional Visual Concepts (Jul, Higgins. et. al.) [[paper]]( https://arxiv.org/abs/1707.03389)

* __***__ DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (Jul, Higgins et. al.) [[paper]](https://arxiv.org/abs/1707.08475)

* ** Unsupervised Learning via Total Correlation Explanation (Jun, Ver Steeg) [[paper]](https://arxiv.org/abs/1706.08984) [[code]](https://github.com/gregversteeg/CorEx)

* ? PixelGAN Autoencoders (Jun, Makhzani & Frey) [[paper]](https://arxiv.org/abs/1706.00531)

* ? Emergence of Invariance and Disentanglement in Deep Representations (Jun, Achille & Soatto) [[paper]](https://arxiv.org/abs/1706.01350)

* ** A Simple Neural Network Module for Relational Reasoning (Jun, Santoro et. al.) [[paper]](https://arxiv.org/abs/1706.01427)

* ? Learning Disentangled Representations with Semi-Supervised Deep Generative Models  (Jun, Siddharth, et al.)  [[paper]](https://arxiv.org/abs/1706.00400)

* ? Unsupervised Learning of Disentangled Representations from Video (May, Denton & Birodkar) [[paper]](https://arxiv.org/abs/1705.10915)



## 2016



* ** Deep Variational Information Bottleneck (Dec, Alemi et. al.) [[paper]](https://arxiv.org/abs/1612.00410)

* __***__ β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (Nov, Higgins et. al.) [[paper]](https://openreview.net/forum?id=Sy2fzU9gl) [[code]](https://github.com/sootlasten/beta-vae)

* ? Disentangling factors of variation in deep representations using adversarial training (Nov, Mathieu et. al.) [[paper]](https://arxiv.org/abs/1611.03383)

* ** Information Dropout: Learning Optimal Representations Through Noisy Computation (Nov, Achille & Soatto) [[paper]](https://arxiv.org/abs/1611.01353)

* ** InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (Jun, Chen et. al.) [[paper]](https://arxiv.org/abs/1606.03657)

* __***__ Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (Mar, Eslami et. al.) [[paper]](https://arxiv.org/abs/1603.08575?context=cs)

* __***__ Building Machines That Learn and Think Like People (Apr, Lake et. al.) [[paper]](https://arxiv.org/abs/1604.00289)

* \* Understanding Visual Concepts with Continuation Learning (Feb, Whitney et. al.) [[paper]](https://arxiv.org/abs/1602.06822)

* ? Disentangled Representations in Neural Models (Feb, Whitney) [[paper]](https://arxiv.org/abs/1602.02383v1)



## Older work



* ** Deep Convolutional Inverse Graphics Network (2015, Kulkarni et. al.) [[paper]](https://arxiv.org/abs/1503.03167)

* ? Learning to Disentangle Factors of Variation with Manifold Interaction (2014, Reed et. al.) [[paper]](http://proceedings.mlr.press/v32/reed14.pdf)

* __***__ Representation Learning: A Review and New Perspectives (2013, Bengio et. al.) [[paper]](https://arxiv.org/abs/1206.5538?context=cs)

* ? Disentangling Factors of Variation via Generative Entangling (2012, Desjardinis et. al.) [[paper]](https://arxiv.org/abs/1210.5474)

* __***__ Transforming Auto-encoders (2011, Hinton et. al.) [[paper]](https://www.cs.toronto.edu/~fritz/absps/transauto6.pdf)

* ** Learning Factorial Codes By Predictability Minimization (1992, Schmidhuber) [[paper]](https://www.mitpressjournals.org/doi/pdf/10.1162/neco.1992.4.6.863)

* __***__ Self-Organization in a Perceptual Network (1988, Linsker) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=36)



## Talks



* Building Machines that Learn & Think Like People (2018, Tenenbaum) [[youtube]](https://www.youtube.com/watch?v=RB78vRUO6X8&t=1807s)

* From Deep Learning of Disentangled Representations to Higher-level Cognition (2018, Bengio) [[youtube]](https://www.youtube.com/watch?v=Yr1mOzC93xs)

* What is wrong with convolutional neural nets? (2017, Hinton) [[youtube]](https://www.youtube.com/watch?v=rTawFwUvnLE&t=2152s)