Skip to content

Latest commit

 

History

History
59 lines (51 loc) · 6.11 KB

README.md

File metadata and controls

59 lines (51 loc) · 6.11 KB
Raw

Autoencoders, Manifolds and Representations

Papers

  • Adversarial Autoencoders (2018), Alireza Makhzani [pdf]
  • Generative Adversarial Autoencoder Networks (2018), Ngoc-Trung Tran [pdf]
  • Learning Sparse Latent Representations With the Deep Copula Information Bottleneck (2018), Aleksander Wieczorek, Mario Wieser [pdf]
  • Mapping a Manifold of Perceptual Observations [pdf]
  • On the Latent Space of Wasserstein Auto-Encoders (2018), Paul Rubenstein [pdf]
  • Progressive Growing of GANs for Improved Quality, Stability, and Variation (2018), Samuli Laine [pdf]
  • The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018), Richard Zhang [pdf]
  • Variational Inference: A Review for Statisticians (2018), David M. Blei [pdf]
  • Wasserstein Auto-Encoders (2018), Ilya Tolstikhin [pdf]
  • Adversarially Regularized Autoencoders (2017), Junbo Zhao [pdf]
  • beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (2017), Irina Higgins [pdf]
  • Disentangling by Factorising (2017), Hyunjik Kim [pdf]
  • Disentangled VAE for Image Classification - CS231n (2017), Chris Varano [pdf]
  • InfoVAE: Information Maximizing Variational Autoencoders (2017), Shengjia Zhao [pdf]
  • JADE: Joint Autoencoders for Dis-Entanglement (2017), Amir-Hossein Karimi [pdf]
  • Learning Disentangled Representations from Grouped Observations (2017), Diane Bouchacourt [pdf]
  • Learning Robust Features with Incremental Auto-Encoders (2017), Donghui Wang [pdf]
  • Optimizing The Latent Space of Generative Networks (2017), Armand Joulin [pdf]
  • PixelGAN Autoencoders (2017), Alireza Makhzani [pdf]
  • Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders (2017), Momchil Peychev [pdf]
  • Understanding Disentangling in beta-VAE (2017), Christopher P. Burgess [pdf]
  • An Uncertain Future: Forecasting from Static Images using Variational Autoencoders (2016), Jacob Walker [pdf]
  • Attribute2Image: Conditional Image Generation from Visual Attributes (2016), Xinchen Yan [pdf]
  • Autoencoding beyond pixels using a learned similarity metric (2016), Anders Larsen [pdf]
  • Early Visual Concept Learning with Unsupervised Deep Learning (2016), Irina Higgins [pdf]
  • Generative Visual Manipulation on the Natural Image Manifold (2016), Jun-Yan Zhu [pdf]
  • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016), Xi Chen [pdf]
  • Manifold Regularized Deep Neural Networks using Adversarial Examples (2016), Taehoon Lee [pdf]
  • Semantic Facial Expression Editing using Autoencoded Flow (2016), Raymond Yeh [pdf]
  • Spatial Transformer Networks (2016), Max Jaderberg [pdf]
  • Tutorial on Variational Autoencoders (2016), Carl Doersch [pdf]
  • Unsupervised Cross-Domain Image Generation (2016), Yaniv Taigman [pdf]
  • Deep Convolutional Inverse Graphics Network (2015), Tejas Kulkarni, Will Whitney [pdf]
  • Auto-encoding Variational Bayes (2014), Kingma [pdf]
  • Representation Learning: A Review and New Perspectives (2014), Yoshua Bengio [pdf]
  • What Regularized Auto-Encoders Learn from the Data Generating Distribution (2014), Guillaume Alain and Yoshua Bengio [pdf]
  • Contractive Auto-Encoders (2011), Salah Rifai [pdf]
  • A Tutorial on Energy-Based Learning (2006), Yann LeCun [pdf]

Blogs

  • Introduction to Autoencoders (2018), Jeremy Jordan [Link]
  • Intuitively Understanding Variational Autoencoders (2018), Irhum Shafkat [Link]
  • Variational Autoencoders (2018), Jeremy Jordan [Link]
  • Neural Networks, Manifolds, and Topology (2014), Christopher Olah [Link]

Videos

  • From Deep Learning of Disentangled Representations to Higher-level Cognition [YouTube]
  • Information Theory of Deep Learning [YouTube]
  • The Sparse Manifold Transform [YouTube]

Libraries

  • Disentangled Variational Autoencoders in PyTorch [GitHub]