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.

翻译：本研究深入探讨生成对抗网络(GANs)在不平衡数据集中的应用。我们旨在提升此类数据集中GANs的性能与稳定性。为此，在融合GAN与对比学习的ContraD GAN框架基础上，提出新型网络架构——损伤GAN(Damage GAN)。通过利用对比学习，判别器被训练为能够区分所有给定样本的无监督表示。该方法受视觉表示对比学习简易框架(SimCLR)启发，构建了独特的损失函数。同时引入自损伤对比学习(SDCLR)以进一步优化ContraD GAN模型。与深度卷积GAN(DCGAN)及ContraD GAN等基线模型的对比评估表明，所提出的损伤GAN模型在不平衡数据集上的生成图像分布、模型稳定性及图像质量方面均展现出显著优势。