What are the main challenges of adversarial training in AI?

What are the main challenges of adversarial training in AI?

Adversarial training faces several challenges, including data scarcity, computational cost, and model robustness.

Can you elaborate on the issue of data scarcity?

Certainly! Data scarcity refers to the limited availability of adversarial examples, which makes it hard to train models effectively.

How does computational cost affect adversarial training?

Computational cost is significant because generating adversarial examples and training models on them requires substantial resources.

What strategies can be used to overcome these challenges?

Strategies include using data augmentation, transfer learning, and employing more efficient algorithms for adversarial training.

Are there any real-world applications where adversarial training is crucial?

Yes, adversarial training is crucial in areas like autonomous driving, security systems, and facial recognition to enhance model robustness.