How does adversarial training improve robustness?

What is adversarial training and how does it work?

Adversarial training is a technique used to improve the robustness of machine learning models by exposing them to adversarial examples during training. This helps the model learn to recognize and correctly classify inputs that may be intentionally misleading.

Can you give me an example of an adversarial example?

Sure! An adversarial example might be a slightly altered image of a cat that is misclassified as a dog by the model. These alterations are often imperceptible to humans but can confuse the model.

How does this training help in real-world applications?

In real-world applications, adversarial training helps models become more resilient against attacks, ensuring they perform reliably even when faced with unexpected or malicious inputs.

Are there any limitations to adversarial training?

Yes, while adversarial training improves robustness, it can also lead to overfitting on the adversarial examples, and it may not generalize well to all types of attacks.