Knowledge Distillation Loss

Knowledge distillation loss is a technique used 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 optimizing the distillation process itself, exploring adaptive weighting of losses, and incorporating techniques like attention mechanisms and gradient reweighting to address challenges such as catastrophic forgetting and imbalanced data. This approach is proving valuable across diverse applications, including image recognition, speech processing, and natural language processing, by enabling the deployment of high-performing models on resource-constrained devices or for continual learning scenarios.