How can I improve adversarial training in my models?

How can I improve adversarial training in my models?

Improving adversarial training can involve several strategies, such as increasing the diversity of adversarial examples, using ensemble methods, or applying regularization techniques. What specific challenges are you facing?

I'm struggling with overfitting when using adversarial examples.

Overfitting can be mitigated by using techniques like dropout, data augmentation, or early stopping. Have you tried any of these methods?

I've used data augmentation, but it doesn't seem to help much.

In that case, consider using a more robust adversarial training method, like TRADES or adversarial logit pairing. These can help balance the trade-off between clean and adversarial accuracy.

What are TRADES and adversarial logit pairing?

TRADES stands for 'Tradeoff between Robustness and Accuracy during Adversarial Training,' and it helps in balancing the two. Adversarial logit pairing aligns the logits of clean and adversarial examples, improving robustness. Would you like to dive deeper into either of these methods?