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$\beta$-VAE is a follow-up technique to variational autoencoders that proposes special weighting of the KL divergence term in the VAE loss to obtain disentangled representations.
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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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 · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Cited alongside, same era.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
3d shapes dataset
Chris Burgess and Hyunjik Kim · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Ricky T. Q. Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
Cited alongside, same era.
Fréchet Inception Distance
Neal Jean · 2018
Cited alongside, same era.
Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
Cited alongside, same era.
How good is my gan?
Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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Disentangled vae
Yann Dubois, Aleco Kastanos, Dave Lines, Bart Melman, and Gökçen Eraslan · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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google-research/disentanglement_lib, 2021
Google · 2019
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A sober look at the unsupervised learning of disentangled representations and their evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2020
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Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Towards an interpretable latent space – an intuitive comparison of autoencoders with variational autoencoders
Thilo Spinner, Jonas Körner, Jochen Görtler, and Oliver Deussen · 2018
Cited alongside, same era.
Understanding disentangling in β \beta -vae, 2018a
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner
Cited in the paper.
Understanding disentangling in β \beta -vae
Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner
Cited in the paper.
Dual contradistinctive generative autoencoder
Gaurav Parmar, Dacheng Li, Kwonjoon Lee, and Zhuowen Tu · 2020
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Simple and effective vae training with calibrated decoders
Oleh Rybkin, Kostas Daniilidis, and Sergey Levine · 2020
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pytorch-fid: FID Score for PyTorch
Maximilian Seitzer · 2020
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