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.

本研究探讨了生成对抗网络（GANs）在不平衡数据集中的应用，旨在增强GANs在此类数据集中的性能和稳定性。通过引入一种称为Damage GAN的新型网络架构，该架构建立在ContraD GAN框架之上，无缝融合了GANs和对比学习，我们利用对比学习训练判别器以发展出能够区分所有提供的样本的无监督表示。通过模仿直观的对比学习视觉表示框架（SimCLR），我们提出了一种独特的损失函数，并探索了自我损伤对比学习（SDCLR）的实现，以进一步增强ContraD GAN模型的优化。与基准模型，包括深度卷积GAN（DCGAN）和ContraD GAN进行比较评估，证明了我们提出的模型Damage GAN在应用于不平衡数据集时在生成的图像分布、模型稳定性和图像质量方面具有明显优势。

损伤生成对抗网络：一种用于不平衡数据的生成模型