How does adversarial training work in deep learning?

Can you explain how adversarial training works in deep learning?

Sure! Adversarial training is a technique used to improve the robustness of deep learning models against adversarial attacks. It involves training the model on both original and adversarial examples.

What are adversarial examples?

Adversarial examples are inputs to a model that have been intentionally perturbed to mislead the model into making incorrect predictions.

How does this training process differ from regular training?

In regular training, the model learns from standard data. In adversarial training, it learns to recognize and correctly classify both clean and adversarial examples, enhancing its resilience.

Are there any limitations to adversarial training?

Yes, adversarial training can be computationally expensive and may not generalize well to unseen adversarial examples. It also requires careful tuning of the training process.