Alleviating limit cycling in training GANs with an optimization technique

In this paper, we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks (GANs) through the proposed predictive centripetal acceleration algorithm (PCAA). Specifically, we first derive the upper and lower complexity bounds of PCAA for a general bilinear game, with the last-iterate convergence rate notably improving upon previous results. Then, we combine PCAA with the adaptive moment estimation algorithm (Adam) to propose PCAA-Adam, for practical training of GANs to enhance their generalization capability. Finally, we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games, multivariate Gaussian distributions, and the CelebA dataset, respectively.