GANs Failure Modes: How to Identify and Monitor Them

Generative Adversarial Networks (GANs) are powerful yet challenging models to train due to their dynamic systems, where two sub-networks, a generator and a discriminator, compete to produce realistic data. Training GANs often leads to challenges like mode collapse, where the generator produces limited data types, and convergence failure, where outputs lack diversity and realism. Effective GAN training requires careful tuning of hyperparameters like learning rate and latent space, understanding loss graphs, and employing strategies such as feature matching and historical averaging to stabilize the model. The article explores methods to identify and address these failure modes, emphasizing the importance of loss graphs and intermediate data in evaluating model stability. It also highlights the significance of using experiment tracking tools like Neptune to monitor and visualize training processes, ultimately aiming to enhance GAN performance by applying tailored strategies and ongoing research efforts.