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.
- ** Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (Jun, Kosiorek et. al.) [paper]
- *** Neural Scene Representation and Rendering (Jun, Eslami et. al.) [paper]
- * Learning Disentangled Joint Continuous and Discrete Representations (May, Dupont) [paper]
- ** Understanding disentangling in β-VAE (Apr, Burgess et. al.) [paper]
- ** Unsupervised Representation Learning by Predicting Image Rotations (Mar, Gidaris et. al.) [paper]
- *** Isolating Sources of Disentanglement in Variational Autoencoders (Mar, Chen et. al.) [paper]
- ** Disentangling by Factorising (Feb, Kim & Mnih) [paper]
- ** Disentangling the independently controllable factors of variation by interacting with the world (Feb, Bengio's group) [paper]
- ? Auto-Encoding Total Correlation Explanation (Feb, Gao et. al.) [paper]
- ** The Multi-Entity Variational Autoencoder (Dec, Nash et. al.) [paper]
- ? Learning Independent Causal Mechanisms (Dec, Parascandolo et. al.) [paper]
- * Neural Discrete Representation Learning (Nov, Oord et. al.) [paper]
- ? Disentangled Representations via Synergy Minimization (Oct, Steeg et. al.) [paper]
- * Experiments on the Consciousness Prior (Sep, Bengio & Fedus) [paper]
- ** The Consciousness Prior (Sep, Bengio) [paper]
- * SCAN: Learning Hierarchical Compositional Visual Concepts (Jul, Higgins. et. al.) [paper]
- *** DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (Jul, Higgins et. al.) [paper]
- ? Emergence of Invariance and Disentanglement in Deep Representations (Jun, Achille & Soatto) [paper]
- ** A simple neural network module for relational reasoning (Jun, Santoro et. al.) [paper]
- ? Unsupervised Learning of Disentangled Representations from Video (May, Denton & Birodkar) [paper]
- ** Deep Variational Information Bottleneck (Dec, Alemi et. al.) [paper]
- *** β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (Nov, Higgins et. al.) [paper] [code]
- ** Information Dropout: Learning Optimal Representations Through Noisy Computation (Nov, Achille & Soatto) [paper]
- ** InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (Jun, Chen et. al.) [paper]
- *** Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (Mar, Eslami et. al.) [paper]
- *** Building Machines That Learn and Think Like People (Apr, Lake et. al.) [paper]
- ? Disentangled Representations in Neural Models (Feb, Whitney) [paper]
- ** Deep Convolutional Inverse Graphics Network (2015, Kulkarni et. al.) [paper]
- *** Representation Learning: A Review and New Perspectives (2013, Bengio et. al.) [paper]
- ? Disentangling Factors of Variation via Generative Entangling (2012, Desjardinis et. al.) [paper]
- ** Learning Factorial Codes By Predictability Minimization (1992, Schmidhuber) [paper]
- *** Self-Organization in a Perceptual Network (1988, Linsker) [paper]