How does adversarial training work?

Can you explain how adversarial training works in AI?

Sure! Adversarial training is a technique used to improve the robustness of machine learning models by exposing them to adversarial examples during training.

What are adversarial examples?

Adversarial examples are inputs to a model that have been intentionally modified to cause the model to make a mistake. They are often small perturbations that are imperceptible to humans.

How does this training help improve model performance?

By training on both regular and adversarial examples, the model learns to recognize and correctly classify inputs that may be misleading, thus enhancing its overall performance and reliability.

Are there any limitations to adversarial training?

Yes, adversarial training can be computationally expensive and may not always generalize well to unseen adversarial examples.