Recent studies on KD<cite> [33,</cite> 15] even leverage more sophisticated model-specific distillation loss functions for better performance. Different from previous KD studies which explicitly exploit a distillation loss to minimize the distance between the teacher model and the student model, we propose a new genre of model compression.