Knowledge Distillation

Knowledge Distillation is a model compression technique that transfers knowledge from a large, complex teacher model to a smaller, more efficient student model by training the student to mimic the teacher’s behavior and output distributions. This process involves training the student model on both ground-truth labels and the soft probability distributions generated by the teacher, enabling the compact model to achieve performance closer to the larger model while maintaining significantly reduced computational requirements.

Knowledge distillation leverages the rich information contained in teacher model predictions, including confidence scores and inter-class relationships that provide more nuanced learning signals than hard labels alone. The technique encompasses various approaches including response-based distillation, feature-based distillation, and attention transfer that optimize different aspects of knowledge transfer. Advanced distillation methods incorporate techniques like progressive distillation, multi-teacher ensembles, and self-distillation to enhance compression effectiveness while preserving model capabilities across diverse applications.