Supervised Knowledge Distillation

Supervised knowledge distillation focuses on transferring knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance while reducing computational costs. Recent research emphasizes self-supervised learning techniques to enhance distillation, particularly in scenarios with limited labeled data, and explores diverse applications including speech-to-speech translation, cross-domain text classification, and medical image segmentation. These advancements are significant because they enable the deployment of high-performing models in resource-constrained environments and improve the efficiency of training complex models, impacting various fields from computer vision to natural language processing.