  This study delves into the application of Generative Adversarial Networks
(GANs) within the context of imbalanced datasets. Our primary aim is to enhance
the performance and stability of GANs in such datasets. In pursuit of this
objective, we introduce a novel network architecture known as Damage GAN,
building upon the ContraD GAN framework which seamlessly integrates GANs and
contrastive learning. Through the utilization of contrastive learning, the
discriminator is trained to develop an unsupervised representation capable of
distinguishing all provided samples. Our approach draws inspiration from the
straightforward framework for contrastive learning of visual representations
(SimCLR), leading to the formulation of a distinctive loss function. We also
explore the implementation of self-damaging contrastive learning (SDCLR) to
further enhance the optimization of the ContraD GAN model. Comparative
evaluations against baseline models including the deep convolutional GAN
(DCGAN) and ContraD GAN demonstrate the evident superiority of our proposed
model, Damage GAN, in terms of generated image distribution, model stability,
and image quality when applied to imbalanced datasets.
