By Minghao Zhang. Since the author only focuses on specific directions, so it just covers small numbers of deep learning areas. If there is anything wrong and missed, just let me know!
Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods, arXiv 2019
Representation Learning: A Review and New Perspectives, TPAMI 2013
Self-supervised Learning: Generative or Contrastive, arxiv
Made: Masked autoencoder for distribution estimation, ICML 2015
Wavenet: A generative model for raw audio, arxiv
Pixel Recurrent Neural Networks, ICML 2016
Conditional Image Generation withPixelCNN Decoders, NeurIPS 2016
Pixelsnail: An improved autoregressive generative model, ICML 2018
Parallel Multiscale Autoregressive Density Estimation, arxiv
Improved Variational Inferencewith Inverse Autoregressive Flow, NeurIPS 2016
Glow: Generative Flowwith Invertible 1×1 Convolutions, NeurIPS 2018
Masked Autoregressive Flow for Density Estimation, NeurIPS 2017
Neural Discrete Representation Learning, NeurIPS 2017
Unsupervised Visual Representation Learning by Context Prediction, ICCV 2015
Distributed Representations of Words and Phrasesand their Compositionality, NeurIPS 2013
Representation Learning withContrastive Predictive Coding, arxiv
Contrastive Multiview Coding, ICLR 2020
Momentum Contrast for Unsupervised Visual Representation Learning, arxiv
A Simple Framework for Contrastive Learning of Visual Representations, arxiv
Contrastive Representation Distillation, ICLR 2020
Neural Predictive Belief Representations, arxiv
World Discovery Models, ICML 2019
Deep Variational Information Bottleneck, ICLR 2017
Learning deep representations by mutual information estimation and maximization, ICLR 2019
Putting An End to End-to-End:Gradient-Isolated Learning of Representations, NeurIPS 2019
What Makes for Good Views for Contrastive Learning?, arxiv
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning, arxiv
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification, ECCV 2020
Improving Unsupervised Image Clustering With Robust Learning, CVPR 2021
InfoBot: Transfer and Exploration via the Information Bottleneck, ICLR 2019
Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR 2017
World Models, arxiv
Learning Latent Dynamics for Planning from Pixels, ICML 2019
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images, NeurIPS 2015
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning, ICML 2017
Count-Based Exploration with Neural Density Models, ICML 2017
Learning Actionable Representations with Goal-Conditioned Policies, ICLR 2019
Automatic Goal Generation for Reinforcement Learning Agents, ICML 2018
VIME: Variational Information Maximizing Exploration, NeurIPS 2017
Unsupervised State Representation Learning in Atari, NeurIPS 2019
Learning Invariant Representations for Reinforcement Learning without Reconstruction, arxiv
CURL: Contrastive Unsupervised Representations for Reinforcement Learning, arxiv
DeepMDP: Learning Continuous Latent Space Models for Representation Learning, ICML 2019
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, ICLR 2017
Isolating Sources of Disentanglement in Variational Autoencoders, NeurIPS 2018
Disentangling by Factorising, ICML 2018
InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets, NeurIPS 2016
Spatial Broadcast Decoder: A Simple Architecture forLearning Disentangled Representations in VAEs, arxiv
Challenging Common Assumptions in the Unsupervised Learning ofDisentangled Representations, ICML 2019
Contrastive Learning of Structured World Models , ICLR 2020
Entity Abstraction in Visual Model-Based Reinforcement Learning, CoRL 2019
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning, ICLR 2019
Object-oriented state editing for HRL, NeurIPS 2019
MONet: Unsupervised Scene Decomposition and Representation, arxiv
Multi-Object Representation Learning with Iterative Variational Inference, ICML 2019
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, ICLR 2020
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation, ICML 2019
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition, arxiv
Object-Oriented Dynamics Predictor, NeurIPS 2018
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions, ICLR 2018
Unsupervised Video Object Segmentation for Deep Reinforcement Learning, NeurIPS 2018
Object-Oriented Dynamics Learning through Multi-Level Abstraction, AAAI 2019
Language as an Abstraction for Hierarchical Deep Reinforcement Learning, NeurIPS 2019
Interaction Networks for Learning about Objects, Relations and Physics, NeurIPS 2016
Learning Compositional Koopman Operators for Model-Based Control, ICLR 2020
Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences, arxiv
Graph Representation Learning, NeurIPS 2019
Workshop on Representation Learning for NLP, ACL 2016-2020