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To combine explicit and implicit generative models, we introduce semi-implicit generator (SIG) as a flexible hierarchical model that can be trained in the maximum likelihood framework.
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On distinguishability criteria for estimating generative models
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Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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Ben Poole, Alexander A Alemi, Jascha Sohl-Dickstein, and Anelia Angelova · 2016
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On the discrimination-generalization tradeoff in gans
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkoz, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
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Maximum-likelihood augmented discrete generative adversarial networks
Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio · 2017
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Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
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Improved training of wasserstein gans
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