Distillation Scheme

Knowledge distillation (KD) aims to transfer knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance and reducing computational costs. Current research focuses on improving KD's robustness to adversarial attacks, developing theoretical foundations for its effectiveness, and exploring novel distillation schemes such as those based on mutual information maximization, transformed teacher matching, and rendering-assisted techniques across various modalities (e.g., image, text). These advancements are significant for deploying AI models on resource-constrained devices and accelerating the training process, impacting both the efficiency and scalability of machine learning applications.