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.

研究表明知识蒸馏不仅可以提高模型性能，还可以增强模型的可解释性。通过量化比较模型可解释性中概念探测器数量的改变，研究表明老师模型传递到学生模型的类相似信息可以提高模型可解释性。这一结论得到了通过定量和定性实验以及不同数据集、不同 KD 方法和不同可解释性指标的检验。结果表明，通过 KD，大模型训练的模型可更可靠地用于各种领域。