On the Impact of Knowledge Distillation for Model Interpretability
Hyeongrok Han Siwon Kim Hyun-Soo Choi Sungroh Yoon
Several recent studies have elucidated why knowledge distillation (KD)
improves model performance. However, few have researched the other advantages
of KD in addition to its improving model performance. In this study, we have
attempted to show that KD enhances the interpretability as well as the accuracy
of models. We measured the number of concept detectors identified in network
dissection for a quantitative comparison of model interpretability. We
attributed the improvement in interpretability to the class-similarity
information transferred from the teacher to student models. First, we confirmed
the transfer of class-similarity information from the teacher to student model
via logit distillation. Then, we analyzed how class-similarity information
affects model interpretability in terms of its presence or absence and degree
of similarity information. We conducted various quantitative and qualitative
experiments and examined the results on different datasets, different KD
methods, and according to different measures of interpretability. Our research
showed that KD models by large models could be used more reliably in various
fields.