saharudra / binding-problem

List of papers on disentangled representation learning.

2019

• Learning Disentangled Representations with Reference-Based Variational Autoencoders (Ruiz, Martinez et. al) [paper]
• Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs (Watters, Burgess, Lercher) ((paper))[https://arxiv.org/abs/1901.07017]

2018

• Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their interaction (Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber) [paper]
• Curiosity Driven Exploration of Learned Disentangled Goal Spaces [paper]
• Recurrent World Models Facilitate Policy Evolution (David Ha, Jürgen Schmidhuber) [paper]
• Hyperprior Induced Unsupervised Disentanglement of Latent Representations (Jan, Ansari and Soh) [paper]
• A Spectral Regularizer for Unsupervised Disentanglement (Dec, Ramesh et. al.) [paper]
• Disentangling Disentanglement (Dec, Mathieu et. al.) [paper]
• Recent Advances in Autoencoder-Based Representation Learning (Dec, Tschannen et. al.) [paper]
• Visual Object Networks: Image Generation with Disentangled 3D Representation (Dec, Zhu et. al.) [paper]
• Towards a Definition of Disentangled Representations (Dec, Higgins et. al.) [paper]
• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (Dec, Locatello et. al.) [paper]
• Learning Deep Representations by Mutual Information Estimation and Maximization (Aug, Hjelm et. al.) [paper]
• Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (Aug, Achille et. al.) [paper]
• Learning to Decompose and Disentangle Representations for Video Prediction (Hsieh et. al.) [paper]
• Insights on Representational Similarity in Neural Networks with Canonical Correlation (Jun, Morcos et. al.) [paper]
• Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (Jun, Kosiorek et. al.) [paper]
• Neural Scene Representation and Rendering (Jun, Eslami et. al.) [paper]
• Image-to-image translation for cross-domain disentanglement (May, Gonzalez-Garcia et. al.) [paper]
• Learning Disentangled Joint Continuous and Discrete Representations (May, Dupont) [paper] [code]
• DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis (Apr, Bem et. al.) [paper]
• Structured Disentangled Representations (Apr, Esmaeili et. al.) [paper]
• Understanding disentangling in β-VAE (Apr, Burgess et. al.) [paper]
• On the importance of single directions for generalization (Mar, Morcos et. al.) [paper]
• Unsupervised Representation Learning by Predicting Image Rotations (Mar, Gidaris et. al.) [paper]
• Disentangled Sequential Autoencoder (Mar, Li & Mandt) [paper]
• Isolating Sources of Disentanglement in Variational Autoencoders (Mar, Chen et. al.) [paper] [code] -- &&
• Disentangling by Factorising (Feb, Kim & Mnih) [paper]
• Disentangling the Independently Controllable Factors of Variation by Interacting with the World (Feb, Bengio's group) [paper]
• On the Latent Space of Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [paper]
• Auto-Encoding Total Correlation Explanation (Feb, Gao et. al.) [paper]
• Fixing a Broken ELBO (Feb, Alemi et. al.) [paper] -- &
• Learning Disentangled Representations with Wasserstein Auto-Encoders (Feb, Rubenstein et. al.) [paper]
• Rethinking Style and Content Disentanglement in Variational Autoencoders (Feb, Shu et. al.) [paper]
• A Framework for the Quantitative Evaluation of Disentangled Representations (Feb, Eastwood & Williams) [paper]
• Disentangling factors of variation by mixing them [paper] -- &&

2017

• Neural Expectation Maximization (Klaus Greff et. al.) [paper]
• The β-VAE's Implicit Prior (Dec, Hoffman et. al.) [paper]
• The Multi-Entity Variational Autoencoder (Dec, Nash et. al.) [paper]
• Learning Independent Causal Mechanisms (Dec, Parascandolo et. al.) [paper]
• Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (Nov, Kumar et. al.) [paper]
• Neural Discrete Representation Learning (Nov, Oord et. al.) [paper]
• Disentangled Representations via Synergy Minimization (Oct, Steeg et. al.) [paper]
• Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data (Sep, Hsu et. al.) [paper] [code]
• Experiments on the Consciousness Prior (Sep, Bengio & Fedus) [paper]
• The Consciousness Prior (Sep, Bengio) [paper]
• Disentangling Motion, Foreground and Background Features in Videos (Jul, Lin. et. al.) [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]
• Unsupervised Learning via Total Correlation Explanation (Jun, Ver Steeg) [paper] [code]
• PixelGAN Autoencoders (Jun, Makhzani & Frey) [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] -- &
• Learning Disentangled Representations with Semi-Supervised Deep Generative Models (Jun, Siddharth, et al.) [paper]
• Unsupervised Learning of Disentangled Representations from Video (May, Denton & Birodkar) [paper]

2016

• Tagger: Deep Unsupervised Perceptual Grouping (Klaus Greff, Jurguen Schmidhuber et. al.) ((paper))[https://papers.nips.cc/paper/6067-tagger-deep-unsupervised-perceptual-grouping.pdf]
• Deep Variational Information Bottleneck (Dec, Alemi et. al.) [paper] -- & (Didn't understand some parts of it)
• β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (Nov, Higgins et. al.) [paper] [code] -- &&
• Disentangling factors of variation in deep representations using adversarial training (Nov, Mathieu et. al.) [paper]
• 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]
• Understanding Visual Concepts with Continuation Learning (Feb, Whitney et. al.) [paper]
• Disentangled Representations in Neural Models (Feb, Whitney) [paper]

Older work

• Deep Convolutional Inverse Graphics Network (2015, Kulkarni et. al.) [paper] -- &
• Learning to Disentangle Factors of Variation with Manifold Interaction (2014, Reed 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]
• Transforming Auto-encoders (2011, Hinton et. al.) [paper] -- &&
• Learning Factorial Codes By Predictability Minimization (1992, Schmidhuber) [paper]
• Self-Organization in a Perceptual Network (1988, Linsker) [paper]