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The focus of disentanglement approaches has been on identifying independent factors of variation in data.
Learning invariance from transformation sequences
Földiák, P · 1991
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Independent component analysis, a new concept?
Comon, P · 1994
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Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P · 1999
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Advances in nonlinear blind source separation
Jutten, C. and Karhunen, J · 2003
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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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Estimation of stature by foot length
Agnihotri, A. K., Purwar, B., Googoolye, K., Agnihotri, S., and Jeebun, N · 2007
Earlier work this paper cites.
Learning graphical model structure using l1-regularization paths
Schmidt, M., Niculescu-Mizil, A., Murphy, K., et al · 2007
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Correlation of foot length with height and weight in school age children
Grivas, T. B., Mihas, C., Arapaki, A., and Vasiliadis, E · 2008
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A 3d face model for pose and illumination invariant face recognition
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., and Vetter, T · 2009
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, 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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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Deep visual analogy-making
Reed, S., Zhang, Y., Zhang, Y., and Lee, H · 2015
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Hyvarinen, A. and Morioka, H · 2016
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Avoiding discrimination through causal reasoning
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B · 2017
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Elements of Causal Inference - Foundations and Learning Algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Discovering interpretable representations for both deep generative and discriminative models
Adel, T., Ghahramani, Z., and Weller, A · 2018
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Bouchacourt, D., Tomioka, R., and Nowozin, S · 2018
Cited alongside, same era.
Understanding disentangling in beta-VAE
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2018
Cited alongside, same era.
Factorised spatial representation learning: Application in semi-supervised myocardial segmentation
Chartsias, A., Joyce, T., Papanastasiou, G., Semple, S., Williams, M., Newby, D., Dharmakumar, R., and Tsaftaris, S. A · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R., and Duvenaud, D · 2018
Cited alongside, same era.
A framework for the quantitative evaluation of disentangled representations
Eastwood, C. and Williams, C. K · 2018
Cited alongside, same era.
Variational autoencoders recover pca directions (by accident)
Rolinek, M., Zietlow, D., and Martius, G · 2019
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Interventional robustness of deep latent variable models
Suter, R., Miladinović, D., Bauer, S., and Schölkopf, B · 2019
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A meta-transfer objective for learning to disentangle causal mechanisms
Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C · 2020
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Variational autoencoders and nonlinear ica: A unifying framework
Khemakhem, I., Kingma, D., Monti, R., and Hyvarinen, A · 2020
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Structured representation learning using structural autoencoders and hybridization
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Scan: Learning hierarchical compositional visual concepts
Higgins, I., Sonnerat, N., Matthey, L., Pal, A., Burgess, C. P., Bošnjak, M., Shanahan, M., Botvinick, M., Hassabis, D., and Lerchner, A · 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.
Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
Cited alongside, same era.
Bias and generalization in deep generative models: An empirical study
Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N., and Ermon, S · 2018
Cited alongside, same era.
Exact rate-distortion in autoencoders via echo noise
Brekelmans, R., Moyer, D., Galstyan, A., and Ver Steeg, G · 2019
Cited alongside, same era.
Flexibly fair representation learning by disentanglement
Creager, E., Madras, D., Jacobsen, J.-H., Weis, M. A., Swersky, K., Pitassi, T., and Zemel, R · 2019
Cited alongside, same era.
Leeb, F., Annadani, Y., Bauer, S., and Schölkopf, B · 2020
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Disentangled state space representations
Miladinović, Đ., Gondal, M. W., Schölkopf, B., Buhmann, J. M., and Bauer, S · 2020
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Weakly supervised disentanglement with guarantees
Shu, R., Chen, Y., Kumar, A., Ermon, S., and Poole, B · 2020
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Disentanglement by nonlinear ica with general incompressible-flow networks (gin)
Sorrenson, P., Rother, C., and Köthe, U · 2020
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Independent subspace analysis for unsupervised learning of disentangled representations
Stühmer, J., Turner, R., and Nowozin, S · 2020
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Causalvae: Structured causal disentanglement in variational autoencoder
Yang, M., Liu, F., Chen, Z., Shen, X., Hao, J., and Wang, J · 2020
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Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Bengio, Y., Schölkopf, B., Wüthrich, M., and Bauer, S · 2021
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Recurrent independent mechanisms
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B · 2021
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Towards nonlinear disentanglement in natural data with temporal sparse coding
Klindt, D., Schott, L., Sharma, Y., Ustyuzhaninov, I., Brendel, W., Bethge, M., and Paiton, D · 2021
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Spatial dependency networks: Neural layers for improved generative image modeling
Miladinović, Đ., Stanić, A., Bauer, S., Schmidhuber, J., and Buhmann, J. M · 2021
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Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y · 2021
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Evaluating the disentanglement of deep generative models through manifold topology
Zhou, S., Zelikman, E., Lu, F., Ng, A. Y., and Ermon, S · 2021
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