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Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an auto-encoder.
Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
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Gradient estimation
M. C. Fu · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Statistical inference for noisy nonlinear ecological dynamic systems
S. N. Wood · 2010
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
M. U. Gutmann and A. Hyvärinen · 2012
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Density ratio estimation in machine learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
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Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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The elements of statistical learning
T. Hastie, R. Tibshirani, and J. Friedman · 2013
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Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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On distinguishability criteria for estimating generative models
I. J. Goodfellow · 2014
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Statistical inference of intractable generative models via classification
M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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A note on the evaluation of generative models
L. Theis, A. v. d. Oord, and M. Bethge · 2015
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Conditional image synthesis with auxiliary classifier GANs
A. Odena, C. Olah, and J. Shlens · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Generative adversarial nets from a density ratio estimation perspective
M. Uehara, I. Sato, M. Suzuki, K. Nakayama, and Y. Matsuo · 2016
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li · 2016
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J. Donahue, P. Krähenbühl, and T. Darrell · 2016
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Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
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Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville · 2016
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Likelihood-free inference by penalised logistic regression
R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann · 2016
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Adversarial message passing for graphical models
T. Karaletsos · 2016
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Autoencoding beyond pixels using a learned similarity metric
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther · 2016
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BEGAN: Boundary equilibrium generative adversarial networks
D. Berthelot, T. Schumm, and L. Metz · 2017
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Comparison of Maximum Likelihood and GAN-based training of Real NVPs
I. Danihelka, B. Lakshminarayanan, B. Uria, D. Wierstra, and P. Dayan · 2017
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Improved Training of Wasserstein GANs
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
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Variational inference using implicit distributions
F. Huszár · 2017
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Learning to discover cross-domain relations with generative adversarial networks
T. Kim, M. Cha, H. Kim, J. Lee, and J. Kim · 2017
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Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
L. Mescheder, S. Nowozin, and A. Geiger · 2017
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Deep and hierarchical implicit models
D. Tran, R. Ranganath, and D. M. Blei · 2017
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Adversarial generator-encoder networks
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2017
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Improving generative adversarial networks with denoising feature matching
D. Warde-Farley and Y. Bengio · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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