Multi-Generator Generative Adversarial Nets

  title={Multi-Generator Generative Adversarial Nets},
  author={Quan Hoang and Tu Dinh Nguyen and Trung Le and Dinh Q. Phung},
We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a… CONTINUE READING
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Improving generative adversarial networks with denoising feature matching. 2016

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