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Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario.
Standardization of progressive matrices
Raven, J. C · 1941
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Fast exact multiplication by the hessian
Pearlmutter, B. A · 1994
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Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., and Bottou, L · 2004
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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3d object detection and viewpoint estimation with a deformable 3d cuboid model
Fidler, S., Dickinson, S., and Urtasun, R · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Deep visual analogy-making
Reed, S. E., Zhang, Y., Zhang, Y., and Lee, H · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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On the relation between accuracy and fairness in binary classification
Zliobaite, I · 2015
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Radford, A., Metz, L., and Chintala, S · 2016
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Generative visual manipulation on the natural image manifold
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., and Efros, A. A · 2016
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Understanding disentangling in β \beta -VAE
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2017
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beta-vae: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning
Kilbertus, N., Carulla, M. R., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B · 2017
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P · 2017
Cited alongside, same era.
3D Shapes Dataset
Burgess, C. and Kim, H · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, R. T., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., and Torralba, A · 2019
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Structured disentangled representations
Esmaeili, B., Wu, H., Jain, S., Bozkurt, A., Siddharth, N., Paige, B., Brooks, D. H., Dy, J., and Meent, J.-W · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., and Bauer, S · 2019
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A style-based generator architecture for Generative Adversarial Networks
Karras, T., Laine, S., and Aila, T · 2019
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Disentangling disentanglement in variational autoencoders
Mathieu, E., Rainforth, T., Siddharth, N., and Teh, Y. W · 2019
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Cited alongside, same era.
Inverting the generator of a generative adversarial network
Creswell, A. and Bharath, A. A · 2018
Cited alongside, same era.
Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Cited alongside, same era.
Art of singular vectors and universal adversarial perturbations
Khrulkov, V. and Oseledets, I · 2018
Cited alongside, same era.
Disentangling by Factorising
Kim, H. and Mnih, A · 2018
Cited alongside, same era.
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
Cited alongside, same era.
Rolinek, M., Zietlow, D., and Martius, G · 2019
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Are Disentangled Representations Helpful for Abstract Visual Reasoning?
van Steenkiste, S., Locatello, F., Schmidhuber, J., and Bachem, O · 2019
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GANSpace: Discovering Interpretable GAN Controls
Härkönen, E., Hertzmann, A., Lehtinen, J., and Paris, S · 2020
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Analyzing and improving the image quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
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Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling GANs
Lin, Z., Thekumparampil, K. K., Fanti, G., and Oh, S · 2020
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A sober look at the unsupervised learning of disentangled representations and their evaluation
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2020
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Semi-Supervised StyleGAN for Disentanglement Learning
Nie, W., Karras, T., Garg, A., Debhath, S., Patney, A., Patel, A. B., and Anandkumar, A · 2020
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The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement
Peebles, W., Peebles, J., Zhu, J.-Y., Efros, A. A., and Torralba, A · 2020
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Closed-Form Factorization of Latent Semantics in GANs
Shen, Y. and Zhou, B · 2020
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Interpreting the latent space of gans for semantic face editing
Shen, Y., Gu, J., Tang, X., and Zhou, B · 2020
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Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Voynov, A. and Babenko, A · 2020
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In-domain GAN Inversion for Real Image Editing
Zhu, J., Shen, Y., Zhao, D., and Zhou, B · 2020
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