Is adversarial training always effective?

What is adversarial training and how does it work?

Adversarial training is a technique used to improve the robustness of machine learning models by training them on adversarial examples—inputs intentionally designed to mislead the model.

Are there situations where adversarial training might not be effective?

Yes, adversarial training may not be effective in cases where the adversarial examples are too different from the real data or when the model overfits to the adversarial examples.

What are some alternatives to adversarial training?

Alternatives include data augmentation, ensemble methods, and using robust optimization techniques to enhance model resilience.

Can adversarial training make a model worse?

Yes, if not done properly, adversarial training can lead to overfitting on adversarial examples, potentially degrading performance on normal data.