Model Robustness

Model robustness, the ability of machine learning models to maintain accuracy under various perturbations or distribution shifts, is a critical area of research aiming to improve the reliability and safety of AI systems. Current efforts focus on enhancing robustness against adversarial attacks (using techniques like adversarial training and regularization of input gradients), improving generalization across diverse datasets (through methods such as data augmentation and synthetic data generation), and developing efficient robustness evaluation methods. These advancements are crucial for deploying reliable AI in safety-critical applications like healthcare, autonomous driving, and finance, where model failures can have significant consequences.