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Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning.
Natural image statistics and neural representation
Simoncelli, Eero and Olshausen, Bruno · 2001
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Multiscale structural similarity for image quality assessment
Wang, Zhou, Simoncelli, Eero P, and Bovik, Alan C · 2004
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Better mixing via deep representations
Bengio, Yoshua, Mesnil, Grégoire, Dauphin, Yann, and Rifai, Salah · 2012
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Auto-Encoding Variational Bayes
Kingma, D. P and Welling, M · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, Andrew, Hannun, Awni, and Ng, Andrew · 2013
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Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Semi-supervised learning with deep generative models
Kingma, Diederik P., Rezende, Danilo Jimenez, Mohamed, Shakir, and Welling, Max · 2014
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Conditional generative adversarial nets
Mirza, Mehdi and Osindero, Simon · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D., Mohamed, S., and Wierstra, D · 2014
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Sequence to sequence learning with neural networks
Sutskever, Ilya, Vinyals, Oriol, and V., Le Quoc · 2014
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Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott E., Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew · 2014
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Density modeling of images using a generalized normalization transformation
Ballé, Johannes, Laparra, Valero, and Simoncelli, Eero P · 2015
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, Emily L., Chintala, Soumith, Szlam, Arthur, and Fergus, Robert · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
Cited alongside, same era.
Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, Olaf, Fischer, Philipp, and Brox, Thomas · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
Adversarially Learned Inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A · 2016
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Improving variational inference with inverse autoregressive flow
Kingma, Diederik P., Salimans, Tim, and Welling, Max · 2016
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W · 2016
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Group mad competition - a new methodology to compare objective image quality models
Ma, Kede, Wu, Qingbo, Wang, Zhou, Duanmu, Zhengfang, Yong, Hongwei, Li, Hongliang, and Zhang, Lei · 2016
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Learning in implicit generative models
Mohamed, Shakir and Lakshminarayanan, Balaji · 2016
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Cited alongside, same era.
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Springenberg, J. T · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, Christian, Vanhoucke, Vincent, Ioffe, Sergey, Shlens, Jonathon, and Wojna, Zbigniew · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2015
Cited alongside, same era.
Model-Free Episodic Control
Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z, Rae, J., Wierstra, D., and Hassabis, D · 2016
Cited alongside, same era.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Density estimation using real NVP
Dinh, Laurent, Sohl-Dickstein, Jascha, and Bengio, Samy · 2016
Cited alongside, same era.
Adversarial Feature Learning
Donahue, J., Krähenbühl, P., and Darrell, T · 2016
Cited alongside, same era.
Nguyen, Anh Mai, Dosovitskiy, Alexey, Yosinski, Jason, Brox, Thomas, and Clune, Jeff · 2016
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Semi-Supervised Learning with Generative Adversarial Networks
Odena, A · 2016
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Deconvolution and checkerboard artifacts
Odena, Augustus, Dumoulin, Vincent, and Olah, Chris · 2016
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Massively multitask networks for drug discovery
Ramsundar, Bharath, Kearnes, Steven, Riley, Patrick, Webster, Dale, Konerding, David, and Pande, Vijay · 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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Full resolution image compression with recurrent neural networks
Toderici, George, Vincent, Damien, Johnston, Nick, Hwang, Sung Jin, Minnen, David, Shor, Joel, and Covell, Michele · 2016
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Generative Adversarial Nets from a Density Ratio Estimation Perspective
Uehara, M., Sato, I., Suzuki, M., Nakayama, K., and Matsuo, Y · 2016
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