Training a Generative Adversarial Network (GAN) presents unique challenges that set it apart from more traditional machine learning models. As you work to balance the generator and discriminator, you may encounter several characteristic problems: mode collapse, non-convergence, and oscillatory behavior. Each of these can undermine the quality of your generated data and make training unpredictable.

Mode collapse occurs when the generator finds a small set of outputs that consistently fool the discriminator, so it keeps producing only those outputs. This means the generator ignores much of the data distribution, resulting in a lack of diversity in the generated samples. Non-convergence happens when the generator and discriminator fail to reach a stable equilibrium, their losses may not settle, and the generator never consistently produces realistic outputs. Oscillatory behavior, meanwhile, is when the losses or generated samples cycle or swing back and forth, never stabilizing. This can make it difficult to judge if the model is improving or just getting stuck in a loop.

Summary of GAN Training Challenges:

• Mode collapse:
  • Symptom: generator produces limited or identical outputs regardless of input;
  • Conceptual cause: generator finds a shortcut that reliably fools the discriminator, neglecting data diversity.
• Non-convergence:
  • Symptom: generator and discriminator losses do not stabilize, outputs remain unrealistic;
  • Conceptual cause: training dynamics fail to reach equilibrium, often due to poor hyperparameters or architecture mismatch.
• Oscillatory behavior:
  • Symptom: losses or outputs swing back and forth without settling;
  • Conceptual Cause: adversarial feedback loop causes both networks to continually overcorrect, preventing progress.

You can think of GAN training as a tug-of-war, where the generator and discriminator pull against each other, each trying to outwit the other. If one side becomes too strong, the balance shifts, and training can stall or collapse. If both sides are evenly matched but keep shifting their strategies, you might see oscillations rather than a steady improvement. Achieving the right balance is crucial but often difficult, which is why these training challenges are so common in practice.