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Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation.
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Visual representations: Defining properties and deep approximations
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Ica-based artifact removal diminishes scan site differences in multi-center resting-state fMRI
R. A. Feis et al · 2015
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Anchored correlation explanation: Topic modeling with minimal domain knowledge
R. J. Gallagher, K. Reing, D. Kale, and G. V. Steeg · 2016
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On the emergence of invariance and disentangling in deep representations
A. Achille and S. Soatto · 2017
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Harmonization of multi-site diffusion tensor imaging data
J.-P. Fortin et al · 2017
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Fader networks: Manipulating images by sliding attributes
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Controllable invariance through adversarial feature learning
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C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel · 2015
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Deep variational information bottleneck
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy · 2016
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Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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Information dropout: Learning optimal representations through noisy computation
A. Achille and S. Soatto · 2018
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Learning controllable fair representations
J. Song, P. Kalluri, A. Grover, S. Zhao, and S. Ermon · 2018
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