Fetching the paper…
Reading the bibliography…
Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types.
object image library (coil-100
Sameer A. Nene, Shree K. Nayar, and Hiroshi Murase · 1996
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2015
Earlier work this paper cites.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Cited alongside, same era.
Tutorial on variational autoencoders
Carl Doersch · 2016
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Cited alongside, same era.
Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot, Colin Raffel, Aurko Roy, and Ian Goodfellow · 2018
Later among the works it cites.
Avoiding latent variable collapse with generative skip models
Adji B. Dieng, Yoon Kim, Alexander M. Rush, and David M. Blei · 2018
Later among the works it cites.
Tim Sainburg, Marvin Thielk, Brad Theilman, Benjamin Migliori, and Timothy Gentner · 2018
Later among the works it cites.
Deforming autoencoders: Unsupervised disentangling of shape and appearance
Zhixin Shu, Mihir Sahasrabudhe, Riza Alp Guler, Dimitris Samaras, Nikos Paragios, and Iasonas Kokkinos · 2018
Later among the works it cites.
Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Raymond Yeh, Ziwei Liu, Dan B Goldman, and Aseem Agarwala · 2016
Cited alongside, same era.
Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Diane Bouchacourt, Ryota Tomioka, and Sebastian Nowozin · 2017
Cited alongside, same era.
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron Courville, Ioannis Mitliagkis, and Yoshua Bengio · 2018
Later among the works it cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Later among the works it cites.
On adversarial mixup resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex M Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, and Chris Pal · 2019
Later among the works it cites.