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Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks.
What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)
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Bengio, Y., Courville, A., and Vincent, P · 2013
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Generative adversarial nets
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Auto-encoding variational Bayes
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Semi-supervised learning with deep generative models
Kingma, D., Mohamed, S., Rezende, D. J., and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
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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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Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2015
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
Yang, J., Reed, S. E., Yang, M.-H., and Lee, H · 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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Adversarial autoencoders
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2016
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Variational autoencoder for deep learning of images, labels and captions
Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., and Carin, L · 2016
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Conditional image generation with PixelCNN decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
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Attribute2image: Conditional image generation from visual attributes
Yan, X., Yang, J., Sohn, K., and Lee, H · 2016
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Unsupervised learning of disentangled representations from video
Denton, E. L. et al · 2017
Affectnet: A database for facial expression, valence, and arousal computing in the wild
Mollahosseini, A., Hasani, B., and Mahoor, M. H · 2017
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Learning disentangled representations with semi-supervised deep generative models
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Villegas, R., Yang, J., Hong, S., Lin, X., and Lee, H · 2017
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Bouchacourt, D., Tomioka, R., and Nowozin, S · 2018
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Adversarial feature learning
Donahue, J., Krähenbühl, P., and Darrell, T · 2017
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, 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
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Unsupervised learning of disentangled and interpretable representations from sequential data
Hsu, W.-N., Zhang, Y., and Glass, J · 2017
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Variational inference using implicit distributions
Huszár, F · 2017
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Alice: Towards understanding adversarial learning for joint distribution matching
Li, C., Liu, H., Chen, C., Pu, Y., Chen, L., Henao, R., and Carin, L · 2017
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Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R., and Duvenaud, D · 2018
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Semantically decomposing the latent spaces of generative adversarial networks
Donahue, C., Lipton, Z. C., Balsubramani, A., and McAuley, J · 2018
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Dual swap disentangling
Feng, Z., Wang, X., Ke, C., Zeng, A.-X., Tao, D., and Song, M · 2018
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Image-to-image translation for cross-domain disentanglement
Gonzalez-Garcia, A., van de Weijer, J., and Bengio, Y · 2018
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Disentangling by factorising
Kim, H. and Mnih, A · 2018
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Variational inference of disentangled latent concepts from unlabeled observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
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Symmetric variational autoencoder and connections to adversarial learning
Pu, Y., Chen, L., Dai, S., Wang, W., Li, C., and Carin, L · 2018
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