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Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning.
The mnist database of handwritten digits
LeCun, Y · 1998
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A · 2010
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One shot learning of simple visual concepts
Lake, B., Salakhutdinov, R., Gross, J., and Tenenbaum, J · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D · 2014
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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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Semi-supervised learning with deep generative models
Kingma, D. P., Rezende, D. J., Mohamed, S., 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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Variational autoencoder based anomaly detection using reconstruction probability
An, J. and Cho, S · 2015
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
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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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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., and Wierstra, D · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
Cited alongside, same era.
Neural variational inference for text processing
Miao, Y., Yu, L., and Blunsom, P · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
Cited alongside, same era.
Improving the improved training of wasserstein gans: A consistency term and its dual effect
Wei, X., Gong, B., Liu, Z., Lu, W., and Wang, L · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yang, Y., Feng, C., Shen, Y., and Tian, D · 2018
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Context-encoding variational autoencoder for unsupervised anomaly detection
Zimmerer, D., Kohl, S. A., Petersen, J., Isensee, F., and Maier-Hein, K. H · 2018
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Topic modeling in embedding spaces
Dieng, A. B., Ruiz, F. J., and Blei, D. M · 2019
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An uncertain future: Forecasting from static images using variational autoencoders
Walker, J., Doersch, C., Gupta, A., and Hebert, M · 2016
Cited alongside, same era.
Glsr-vae: Geodesic latent space regularization for variational autoencoder architectures
Hadjeres, G., Nielsen, F., and Pachet, F · 2017
Cited alongside, same era.
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.
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
Cited alongside, same era.
Life-long disentangled representation learning with cross-domain latent homologies
Achille, A., Eccles, T., Matthey, L., Burgess, C. P., Watters, N., Lerchner, A., and Higgins, I · 2018
Cited alongside, same era.
Avoiding latent variable collapse with generative skip models
Dieng, A. B., Kim, Y., Rush, A. M., and Blei, D. M · 2018
Cited alongside, same era.
Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, A. C., Sontag, D., and Rush, A. M · 2018
Cited alongside, same era.
Fang, L., Li, C., Gao, J., Dong, W., and Chen, C · 2019
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Cyclical annealing schedule: A simple approach to mitigating kl vanishing
Fu, H., Li, C., Liu, X., Gao, J., Celikyilmaz, A., and Carin, L · 2019
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Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., and Berg-Kirkpatrick, T · 2019
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Variational adversarial active learning
Sinha, S., Ebrahimi, S., and Darrell, T · 2019
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Unsupervised data augmentation for consistency training
Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q. V · 2019
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Distribution augmentation for generative modeling
Jun, H., Child, R., Chen, M., Schulman, J., Ramesh, A., Radford, A., and Sutskever, I · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Kostrikov, I., Yarats, D., and Fergus, R · 2020
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Regularization with latent space virtual adversarial training
Osada, G., Ahsan, B., Bora, R. P., and Nishide, T · 2020
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Nvae: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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Consistency regularization for generative adversarial networks
Zhang, H., Zhang, Z., Odena, A., and Lee, H · 2020
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S4rl: Surprisingly simple self-supervision for offline reinforcement learning
Sinha, S. and Garg, A · 2021
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