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Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 1906
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 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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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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The mnist database of handwritten digit images for machine learning research [best of the web]
Deng, L · 2012
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Density ratio estimation in machine learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
Aubry, M., Maturana, D., Efros, A. A., Russell, B. C., and Sivic, J · 2014
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 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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Shapenet: An information-rich 3d model repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 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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Deep visual analogy-making
Reed, S. E., Zhang, Y., Zhang, Y., and Lee, H · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 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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Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Disentangling factors of variation in deep representation using adversarial training
Mathieu, M. F., Zhao, J. J., Zhao, J., Ramesh, A., Sprechmann, P., and LeCun, Y · 2016
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Invertible conditional gans for image editing
Perarnau, G., van de Weijer, J., Raducanu, B., and Álvarez, J. M · 2016
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Generative adversarial text to image synthesis
Reed, S. E., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H · 2016
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Feature-wise transformations
Dumoulin, V., Perez, E., Schucher, N., Strub, F., Vries, H. d., Courville, A., and Bengio, Y · 2018
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Occlusion-aware 3d morphable models and an illumination prior for face image analysis
Egger, B., Schönborn, S., Schneider, A., Kortylewski, A., Morel-Forster, A., Blumer, C., and Vetter, T · 2018
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Morphable face models - an open framework
Gerig, T., Morel-Forster, A., Blumer, C., Egger, B., Lüthi, M., Schönborn, S., and Vetter, T · 2018
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Disentangling factors of variation by mixing them
Hu, Q., Szabó, A., Portenier, T., Favaro, P., and Zwicker, M · 2018
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
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Instance normalization: The missing ingredient for fast stylization
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2016
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., and Metaxas, D. N · 2016
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Generating multi-label discrete patient records using generative adversarial networks
Choi, E., Biswal, S., Malin, B. A., Duke, J., Stewart, W. F., and Sun, J · 2017
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GANS trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., 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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Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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Kim, H. and Mnih, A · 2018
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Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision
Lezama, J · 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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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2018
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Challenges in disentangling independent factors of variation
Szabó, A., Hu, Q., Portenier, T., Zwicker, M., and Favaro, P · 2018
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Learning discrete and continuous factors of data via alternating disentanglement
Jeong, Y. and Song, H. O · 2019
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Analyzing and reducing the damage of dataset bias to face recognition with synthetic data
Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., and Vetter, T · 2019
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On the jensen–shannon symmetrization of distances relying on abstract means
Nielsen, F · 2019
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Generating diverse high-fidelity images with vq-vae-2
Razavi, A., van den Oord, A., and Vinyals, O · 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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Modeling Tabular Data Using Conditional GAN
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
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High-fidelity synthesis with disentangled representation
Lee, W., Kim, D., Hong, S., and Lee, H · 2020
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Ctab-gan: Effective table data synthesizing
Zhao, Z., Kunar, A., Birke, R., and Chen, L. Y · 2021
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Tabular data generation: Can we fool XGBoost ?
Zein, E. H. and Urvoy, T · 2022
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