What are the limitations of adversarial training in AI?

What are the limitations of adversarial training in AI?

Adversarial training can be effective, but it has several limitations, such as overfitting to adversarial examples and increased computational costs.

Can you explain how overfitting occurs in adversarial training?

Overfitting in adversarial training happens when the model learns to perform well on the adversarial examples but fails to generalize to normal data.

What are the computational costs associated with adversarial training?

The computational costs include the need for additional training time and resources to generate adversarial examples and train the model on them.

Are there any alternatives to adversarial training?

Yes, alternatives include defensive distillation, input preprocessing, and using robust optimization techniques.

How can I implement these alternatives in my project?

You can start by researching specific libraries and frameworks that support these techniques, and then integrate them into your training pipeline.