Can adversarial training improve model accuracy?

What is adversarial training and how does it work?

Adversarial training is a technique used to improve model robustness by training on adversarial examples, which are inputs designed to fool the model.

Can it really improve the accuracy of a model?

Yes, adversarial training can enhance accuracy, especially in scenarios where models are exposed to adversarial attacks during testing.

Are there any downsides to using adversarial training?

One downside is that it can increase training time and may lead to overfitting on adversarial examples if not balanced properly.

What types of models benefit the most from this technique?

Deep learning models, particularly those used in image and natural language processing, tend to benefit significantly from adversarial training.