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Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data.
Learning the parts of objects by non-negative matrix factorization
Lee, D. D. and Seung, H. S · 1999
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The human visual cortex
Grill-Spector, K. and Malach, R · 2004
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Causality
Pearl, J · 2009
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Basic topology
Armstrong, M. A · 2013
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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 · 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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The causal foundations of structural equation modeling
Pearl, J · 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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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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A neural algorithm of artistic style
Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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Causal inference in statistics, social, and biomedical sciences
Imbens, G. W. and Rubin, D. B · 2015
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Deep convolutional inverse graphics network
Kulkarni, T. D., Whitney, W. F., Kohli, P., and Tenenbaum, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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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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Inverting visual representations with convolutional networks
Dosovitskiy, A. and Brox, T · 2016
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Disentangling factors of variation in deep representation using adversarial training
Elements of Causal Inference – Foundations and Learning Algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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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 · 2018
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Group invariance principles for causal generative models
Besserve, M., Shajarisales, N., Schölkopf, B., and Janzing, D · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
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Mathieu, M. F., Zhao, J. J., Ramesh, A., Sprechmann, P., and LeCun, Y · 2016
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Began: Boundary equilibrium generative adversarial networks
Berthelot, D., Schumm, T., and Metz, L · 2017
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A post-nonlinear mixture model approach to binary matrix factorization
Diop, M., Larue, A., Miron, S., and Brie, D · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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Gans trained by a two time-scale update rule converge to a nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S · 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
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Growing interpretable part graphs on convnets via multi-shot learning
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Modeling visual context is key to augmenting object detection datasets
Dvornik, N., Mairal, J., and Schmid, C · 2018
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Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Gelly, S., Schölkopf, B., and Bachem, O · 2018
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Learning independent causal mechanisms
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Assessing generative models via precision and recall
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Suter, R., Miladinović, D., Schölkopf, B., and Bauer, S · 2018
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