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Gatys et al.
Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling
Zhu, S. C., Wu, Y., and Mumford, D · 1998
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A parametric texture model based on joint statistics of complex wavelet coefficients
Portilla, J. and Simoncelli, E. P · 2000
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A kernel method for the two-sample-problem
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte, Schölkopf, Bernhard, and Smola, Alex J · 2006
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Return of the devil in the details: Delving deep into convolutional nets
Chatfield, Ken, Simonyan, Karen, Vedaldi, Andrea, and Zisserman, Andrew · 2014
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Generative adversarial nets
Goodfellow, Ian J., Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron C., and Bengio, Yoshua · 2014
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Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
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Generative moment matching networks
Li, Yujia, Swersky, Kevin, and Zemel, Richard S · 2015
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Fully convolutional networks for semantic segmentation
Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor · 2015
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Understanding deep image representations by inverting them
Mahendran, Aravindh and Vedaldi, Andrea · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, Alec, Metz, Luke, and Chintala, Soumith · 2015
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Cited alongside, same era.
Texture synthesis using convolutional neural networks
Gatys, Leon, Ecker, Alexander S, and Bethge, Matthias
Cited in the paper.
A neural algorithm of artistic style
Gatys, Leon A., Ecker, Alexander S., and Bethge, Matthias
Cited in the paper.
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