The Bid Picture: Auction-Inspired Multi-player Generative Adversarial
  Networks Training
Joo Yong Shim Jean Seong Bjorn Choe Jong-Kook Kim
This article proposes auction-inspired multi-player generative adversarial
networks training, which mitigates the mode collapse problem of GANs. Mode
collapse occurs when an over-fitted generator generates a limited range of
samples, often concentrating on a small subset of the data distribution.
Despite the restricted diversity of generated samples, the discriminator can
still be deceived into distinguishing these samples as real samples from the
actual distribution. In the absence of external standards, a model cannot
recognize its failure during the training phase. We extend the two-player game
of generative adversarial networks to the multi-player game. During the
training, the values of each model are determined by the bids submitted by
other players in an auction-like process.