Paper ID: 2403.13866

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.

Submitted: Mar 20, 2024