GAN Training

GAN training aims to stabilize and improve the performance of Generative Adversarial Networks, which generate realistic data by pitting a generator against a discriminator in a minimax game. Current research focuses on novel loss functions, regularization techniques (e.g., Lipschitz continuity constraints, score matching), and architectural modifications to address issues like mode collapse, overfitting (especially with limited data), and training instability. These advancements are significant because they enhance the quality and diversity of generated data, impacting diverse fields such as medical imaging, video generation, and image editing, where high-fidelity synthetic data is crucial for various applications.