Damage GAN: A Generative Model for Imbalanced Data

no code implementations • 8 Dec 2023 • Ali Anaissi, Yuanzhe Jia, Ali Braytee, Mohamad Naji, Widad Alyassine

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.

Contrastive Learning