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We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables.
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Geman, Stuart and Geman, Donald · 1984
Earlier work this paper cites.
Statistics of natural images: Scaling in the woods
Ruderman, Daniel L and Bialek, William · 1994
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Natural image statistics and efficient coding
Olshausen, Bruno A and Field, David J · 1996
Earlier work this paper cites.
Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling
Zhu, Song Chun, Wu, Yingnian, and Mumford, David · 1998
Earlier work this paper cites.
Fields of experts: A framework for learning image priors
Roth, Stefan and Black, Michael J · 2005
Earlier work this paper cites.
On the local behavior of spaces of natural images
Carlsson, Gunnar, Ishkhanov, Tigran, De Silva, Vin, and Zomorodian, Afra · 2008
Earlier work this paper cites.
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. , volume 39
Hyvärinen, Aapo, Hurri, Jarmo, and Hoyer, Patrick O · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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From learning models of natural image patches to whole image restoration
Zoran, Daniel and Weiss, Yair · 2011
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Generative adversarial networks
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2014
Cited alongside, same era.
Factoring variations in natural images with deep gaussian mixture models
van den Oord, Aaron and Schrauwen, Benjamin · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, Emily L, Chintala, Soumith, Fergus, Rob, et al · 2015
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NICE: Non-linear independent components estimation
Dinh, Laurent, Krueger, David, and Bengio, Yoshua · 2015
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Draw: A recurrent neural network for image generation
Gregor, Karol, Danihelka, Ivo, Graves, Alex, Rezende, Danilo, and Wierstra, Daan · 2015
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Deep learning face attributes in the wild
Liu, Ziwei, Luo, Ping, Wang, Xiaogang, and Tang, Xiaoou · 2015
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Learning what and where to draw
Reed, Scott E, Akata, Zeynep, Mohan, Santosh, Tenka, Samuel, Schiele, Bernt, and Lee, Honglak · 2016
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Improved techniques for training GANs
Salimans, Tim, Goodfellow, Ian, Zaremba, Wojciech, Cheung, Vicki, Radford, Alec, and Chen, Xi · 2016
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Generative image modeling using style and structure adversarial networks
Wang, Xiaolong and Gupta, Abhinav · 2016
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Variational lossy autoencoder
Chen, Xi, Kingma, Diederik P, Salimans, Tim, Duan, Yan, Dhariwal, Prafulla, Schulman, John, Sutskever, Ilya, and Abbeel, Pieter · 2017
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Pixel recursive super resolution
Dahl, Ryan, Norouzi, Mohammad, and Shlens, Jonathov · 2017
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Density estimation using real NVP
Dinh, Laurent, Sohl-Dickstein, Jascha, and Bengio, Samy · 2017
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, Jascha, Weiss, Eric, Maheswaranathan, Niru, and Ganguli, Surya · 2015
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An architecture for deep, hierarchical generative models
Bachman, Philip · 2016
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Towards conceptual compression
Gregor, Karol, Besse, Frederic, Jimenez Rezende, Danilo, Danihelka, Ivo, and Wierstra, Daan · 2016
Cited alongside, same era.
Improving variational inference with inverse autoregressive flow
Kingma, Diederik P., Salimans, Tim, and Welling, Max · 2016
Cited alongside, same era.
Conditional image generation with pixelCNN decoders
van den Oord, Aaron, Kalchbrenner, Nal, Espeholt, Lasse, Vinyals, Oriol, and Graves, Alex
Cited in the paper.
Pixel recurrent neural networks
van den Oord, Aaron, Kalchbrenner, Nal, and Kavukcuoglu, Koray
Cited in the paper.
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PixelVAE: A latent variable model for natural images
Gulrajani, Ishaan, Kumar, Kundan, Ahmed, Faruk, Taiga, Adrien Ali, Visin, Francesco, Vazquez, David, and Courville, Aaron · 2017
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Fast generation for convolutional autoregressive models
Ramachandran, Prajit, Paine, Tom Le, Khorrami, Pooya, Babaeizadeh, Mohammad, Chang, Shiyu, Zhang, Yang, Hasegawa-Johnson, Mark, Campbell, Roy, and Huang, Thomas · 2017
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Parallel multiscale autoregressive density estimation
Reed, Scott, Oord, Aäron van den, Kalchbrenner, Nal, Colmenarejo, Sergio Gómez, Wang, Ziyu, Belov, Dan, and de Freitas, Nando · 2017
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PixelCNN++: A PixelCNN implementation with discretized logistic mixture likelihood and other modifications
Salimans, Tim, Karpathy, Andrej, Chen, Xi, Knigma, Diederik P., and Bulatov, Yaroslav · 2017
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