Damage GAN: A Generative Model for Imbalanced Data
This addresses the challenge of training GANs on imbalanced data, which is common in real-world applications, though it appears incremental as it builds upon existing ContraD GAN and contrastive learning methods.
The study tackled the problem of improving Generative Adversarial Networks (GANs) for imbalanced datasets by introducing Damage GAN, which outperformed baseline models like DCGAN and ContraD GAN in generated image distribution, stability, and quality.
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.