Non-Adversarial Robustness

Non-Adversarial Robustness refers to the resilience of machine learning models against non-malicious data perturbations and variations. Unlike adversarial robustness, which focuses on defending against intentional attacks, non-adversarial robustness ensures that models maintain accurate performance under natural distortions, noise, and real-world changes in data distribution. This concept is crucial for developing reliable AI systems capable of performing consistently and effectively in diverse environments. By prioritizing non-adversarial robustness, businesses can enhance their machine learning applications, ensuring greater accuracy, stability, and user trust.

‍