How does adversarial training enhance AI model performance?

Adversarial training enhances AI model performance by incorporating both clean data and data that has been intentionally modified to present challenges to the model, known as adversarial examples. This is critical because it allows the AI model to learn to recognize and adapt to inputs that may be deceptive or problematic, which are common in real-world scenarios.

By training on this mix of real and adversarial data, the model becomes more robust and resistant to manipulation, ultimately improving its generalization and accuracy on unseen data. This process exposes the model to a broader range of scenarios, thereby increasing its ability to deal with unexpected variations in the data. This is particularly important in environments where adversarial attacks might occur, ensuring that the model is better prepared to handle such situations effectively.