Xuesheng Zhang

Flow-Based Knowledge Transfer for Efficient Large Model Distillation

Traditional knowledge distillation relies on simple MSE or KL divergence losses that fail to capture the complex distributional relationships between teacher and student model representations. We propose FlowDistill, a novel distillation framework that employs normalizing flows to model and transfer