Robust Model

Robust model research aims to create machine learning models that maintain high performance despite variations in input data, including adversarial attacks and distribution shifts. Current efforts focus on developing training methods that enhance robustness across multiple perturbation types (e.g., multi-norm training), leveraging self-supervised learning for improved feature representation, and exploring the role of pre-training and data balancing in mitigating bias and improving generalization. These advancements are crucial for deploying reliable machine learning systems in real-world applications, particularly in safety-critical domains like healthcare and autonomous driving, where model robustness is paramount.