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The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.
Random forests
Leo Breiman · 2001
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Neural photo editing with introspective adversarial networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Variational lossy autoencoder
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2017
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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
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Denoising criterion for variational auto-encoding framework
Daniel Im Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio · 2017
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Deeppermnet: Visual permutation learning
Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian, and Stephen Gould · 2017
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Jakub M Tomczak and Max Welling · 2017
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Jigsaw puzzle solving using local feature co-occurrences in deep neural networks
Marie-Morgane Paumard, David Picard, and Hedi Tabia · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
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Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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Serena Yeung, Anitha Kannan, Yann Dauphin, and Li Fei-Fei · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Shane Barratt and Rishi Sharma · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach · 2018
Cited alongside, same era.
Hyunjik Kim and Andriy Mnih · 2018
Cited alongside, same era.
Feature-wise bias amplification
Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, and Anupam Datta · 2018
Cited alongside, same era.
All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2019
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Learning not to learn: Training deep neural networks with biased data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim · 2019
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Repair: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos · 2019
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Unsupervised part-based disentangling of object shape and appearance
Dominik Lorenz, Leonard Bereska, Timo Milbich, and Bjorn Ommer · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Latent space expanded variational autoencoder for sentence generation
Tianbao Song, Jingbo Sun, Bo Chen, Weiming Peng, and Jihua Song · 2019
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A framework for understanding unintended consequences of machine learning
Harini Suresh and John V Guttag · 2019
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Variational autoencoder with truncated mixture of gaussians for functional connectivity analysis
Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli, Adolf Pfefferbaum, Edith V Sullivan, and Kilian M Pohl · 2019
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Balancing reconstruction error and kullback-leibler divergence in variational autoencoders
Andrea Asperti and Matteo Trentin · 2020
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