Selective Sampling and Mixture Models in Generative Adversarial Networks

2 Feb 2018 Karim Said Barsim Lirong Yang Bin Yang

In this paper, we propose a multi-generator extension to the adversarial training framework, in which the objective of each generator is to represent a unique component of a target mixture distribution. In the training phase, the generators cooperate to represent, as a mixture, the target distribution while maintaining distinct manifolds... (read more)

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