Robust Classification

Robust classification aims to develop machine learning models that maintain high accuracy even when faced with noisy, incomplete, or adversarially perturbed data. Current research focuses on improving model robustness through techniques like novel loss functions (e.g., bounded loss functions), data augmentation strategies (e.g., label smoothing, image noising), and architectural innovations (e.g., attractor networks, diffusion models, and brain-inspired architectures). These advancements are crucial for deploying reliable machine learning systems in real-world applications where data quality is often imperfect, impacting fields ranging from medical image analysis to autonomous driving.