Understanding GAN Mode Collapse: Causes and Solutions

Generative Adversarial Networks (GANs) face challenges with mode collapse, where they generate limited output instead of diverse data. Two main causes are catastrophic forgetting and discriminator overfitting. Catastrophic forgetting occurs when the model forgets previous knowledge while learning new tasks. Discriminator overfitting leads to vanishing gradients, preventing generated samples from moving toward real data points. These issues result in repetitive outputs lacking variety. Understanding these causes can help develop GANs that produce more diverse and high-quality results.