Fetching the paper…
Reading the bibliography…
Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a (nominally trained) VAE does not necessarily amortize inference for typical samples that it is capable of generating.
Em algorithms for pca and spca
Sam T Roweis · 1998
Earlier work this paper cites.
Probabilistic principal component analysis
Michael E. Tipping and Christopher M. Bishop · 1999
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 2018
Cited alongside, same era.
Understanding disentangling in β \beta -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Recent advances in autoencoder-based representation learning
Michael Tschannen, Olivier Bachem, and Mario Lucic · 2018
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
Later among the works it cites.
Christos Louizos, Xiahan Shi, Klamer Schutte, and Max Welling · 2019
Later among the works it cites.
Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara, and Nicholas Ruozzi · 2019
Later among the works it cites.
Infovae: Balancing learning and inference in variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Disentangling factors of variation with cycle-consistent variational auto-encoders
Ananya Harsh Jha, Saket Anand, Maneesh Singh, and VSR Veeravasarapu · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Adversarially robust representations with smooth encoders
A. T. Cemgil, S. Ghaisas, K. Dvijotham, and P. Kohli · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Closest in time.