HingeRLC-GAN: Combating Mode Collapse with Hinge Loss and RLC Regularization
Journal:
arXiv
Published Date:
Mar 24, 2025
Abstract
Recent advances in Generative Adversarial Networks (GANs) have demonstrated
their capability for producing high-quality images. However, a significant
challenge remains mode collapse, which occurs when the generator produces a
limited number of data patterns that do not reflect the diversity of the
training dataset. This study addresses this issue by proposing a number of
architectural changes aimed at increasing the diversity and stability of GAN
models. We start by improving the loss function with Wasserstein loss and
Gradient Penalty to better capture the full range of data variations. We also
investigate various network architectures and conclude that ResNet
significantly contributes to increased diversity. Building on these findings,
we introduce HingeRLC-GAN, a novel approach that combines RLC Regularization
and the Hinge loss function. With a FID Score of 18 and a KID Score of 0.001,
our approach outperforms existing methods by effectively balancing training
stability and increased diversity.
