Mode Collapse

Mode collapse, a prevalent issue in generative models, occurs when a model fails to capture the full diversity of a data distribution, instead focusing on a limited subset of its modes. Current research focuses on detecting and mitigating this problem across various architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and normalizing flows, employing techniques like adversarial training, entropy maximization, and adaptive input normalization. Overcoming mode collapse is crucial for improving the reliability and generalizability of generative models, impacting applications ranging from data augmentation in medical imaging to controllable text generation and accurate sampling in physics simulations. The development of robust metrics for quantifying mode collapse is also a significant area of ongoing investigation.