disentangled-representation-papers

2018 年 9 月 12 日 CreateAMind
disentangled-representation-papers

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



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.

2018

  • ? 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]

  • ? 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]

2017

  • ? 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

  • ** 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]

  • ? 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]

Talks

  • Building Machines that Learn & Think Like People (2018, Tenenbaum) [youtube]

  • From Deep Learning of Disentangled Representations to Higher-level Cognition (2018, Bengio) [youtube]

  • What is wrong with convolutional neural nets? (2017, Hinton) [youtube]





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Learning compact representation is vital and challenging for large scale multimedia data. Cross-view/cross-modal hashing for effective binary representation learning has received significant attention with exponentially growing availability of multimedia content. Most existing cross-view hashing algorithms emphasize the similarities in individual views, which are then connected via cross-view similarities. In this work, we focus on the exploitation of the discriminative information from different views, and propose an end-to-end method to learn semantic-preserving and discriminative binary representation, dubbed Discriminative Cross-View Hashing (DCVH), in light of learning multitasking binary representation for various tasks including cross-view retrieval, image-to-image retrieval, and image annotation/tagging. The proposed DCVH has the following key components. First, it uses convolutional neural network (CNN) based nonlinear hashing functions and multilabel classification for both images and texts simultaneously. Such hashing functions achieve effective continuous relaxation during training without explicit quantization loss by using Direct Binary Embedding (DBE) layers. Second, we propose an effective view alignment via Hamming distance minimization, which is efficiently accomplished by bit-wise XOR operation. Extensive experiments on two image-text benchmark datasets demonstrate that DCVH outperforms state-of-the-art cross-view hashing algorithms as well as single-view image hashing algorithms. In addition, DCVH can provide competitive performance for image annotation/tagging.

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