  Despite excellent progress in recent years, mode collapse remains a major
unsolved problem in generative adversarial networks (GANs).In this paper, we
present spectral regularization for GANs (SR-GANs), a new and robust method for
combating the mode collapse problem in GANs. Theoretical analysis shows that
the optimal solution to the discriminator has a strong relationship to the
spectral distributions of the weight matrix.Therefore, we monitor the spectral
distribution in the discriminator of spectral normalized GANs (SN-GANs), and
discover a phenomenon which we refer to as spectral collapse, where a large
number of singular values of the weight matrices drop dramatically when mode
collapse occurs. We show that there are strong evidence linking mode collapse
to spectral collapse; and based on this link, we set out to tackle spectral
collapse as a surrogate of mode collapse. We have developed a spectral
regularization method where we compensate the spectral distributions of the
weight matrices to prevent them from collapsing, which in turn successfully
prevents mode collapse in GANs. We provide theoretical explanations for why
SR-GANs are more stable and can provide better performances than SN-GANs. We
also present extensive experimental results and analysis to show that SR-GANs
not only always outperform SN-GANs but also always succeed in combating mode
collapse where SN-GANs fail. The code is available at
https://github.com/max-liu-112/SRGANs-Spectral-Regularization-GANs-.
