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Generative Adversarial Networks (GANs) are a well-known technique that is trained on samples (e.g.
Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero. 2014 · 2014
Earlier work this paper cites.
Conditional generative adversarial nets for convolutional face generation
Jon Gauthier. 2015 · 2015
Cited alongside, same era.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, and Soumith Chintala. 2015 · 2015
Cited alongside, same era.
Deep Convolution Generative Adversarial Networks
Pytorch. 2019 · 2016
Later among the works it cites.
The repo of Street View Image, Pose, and 3D Cities Dataset. Used in "Generic 3D Representation via Pose Estimation and Matching", ECCV16: amir32002/3D_Street_View
Amir Zamir. 2019 · 2017
Later among the works it cites.
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