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Deep generative models are becoming a cornerstone of modern machine learning.
Web-scale k-means clustering
Sculley, D · 2010
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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
Earlier work this paper cites.
Learning to see by moving
Agrawal, P., Carreira, J., and Malik, J · 2015
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Semi-supervised learning with generative adversarial networks
Odena, A · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Pinto, L. and Gupta, A · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Springenberg, J. T · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Modulating early visual processing by language
De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., and Courville, A. C · 2017
Earlier work this paper cites.
Structured generative adversarial networks
Deng, Z., Zhang, H., Liang, X., Yang, L., Xu, S., Zhu, J., and Xing, E. P · 2017
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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A learned representation for artistic style
Dumoulin, V., Shlens, J., and Kudlur, M · 2017
Cited alongside, same era.
Triangle generative adversarial networks
Gan, Z., Chen, L., Wang, W., Pu, Y., Zhang, Y., Liu, H., Li, C., and Carin, L · 2017
Cited alongside, same era.
Accurate, large minibatch SGD: training ImageNet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Video pixel networks
Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2017
Cited alongside, same era.
Unsupervised representation learning by sorting sequences
Grasp2Vec: Learning object representations from self-supervised grasping
Jang, E., Devin, C., Vanhoucke, V., and Levine, S · 2018
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Are GANs created equal? A large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
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cgans with projection discriminator
Miyato, T. and Koyama, M · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Improvements to context based self-supervised learning
Mundhenk, T. N., Ho, D., and Chen, B. Y · 2018
Later among the works it cites.
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
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Lee, H.-Y., Huang, J.-B., Singh, M., and Yang, M.-H · 2017
Cited alongside, same era.
Triple Generative Adversarial Nets
Li, C., Xu, T., Zhu, J., and Zhang, B · 2017
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier GANs
Odena, A., Olah, C., and Shlens, J · 2017
Cited alongside, same era.
Semi-supervised conditional GANs
Sricharan, K., Bala, R., Shreve, M., Ding, H., Saketh, K., and Sun, J · 2017
Cited alongside, same era.
On gradient regularizers for MMD GANs
Arbel, M., Sutherland, D., Bińkowski, M., and Gretton, A · 2018
Cited alongside, same era.
Barratt, S. and Sharma, R · 2018
Cited alongside, same era.
Deep clustering for unsupervised learning of visual features
Caron, M., Bojanowski, P., Joulin, A., and Douze, M · 2018
Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Closest in time.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X., and Beyer, L · 2019
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The GAN Landscape: Losses, architectures, regularization, and normalization
Kurach, K., Lucic, M., Zhai, X., Michalski, M., and Gelly, S · 2019
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Menick, J. and Kalchbrenner, N · 2019
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S 4 L: Self-Supervised Semi-Supervised Learning
Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L · 2019
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2019
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