Extractive Knowledge Distillation

Knowledge distillation (KD) transfers knowledge of a teacher model to improve performance of a student model which is usually equipped with lower capacity. In the KD framework, however, it is unclear what kind of knowledge is effective and how it is transferred. This paper analyzes a KD process to explore the key factors. In a KD formulation, softmax temperature entangles three main components of student and teacher probabilities and a weight for KD, making it hard to analyze contributions of those factors separately. We disentangle those components so as to further analyze especially the temperature and improve the components respectively. Based on the analysis about temperature and uniformity of the teacher probability, we propose a method, called extractive distillation, for extracting effective knowledge from the teacher model. The extractive KD touches only teacher knowledge, thus being applicable to various KD methods. In the experiments on image classification tasks using Cifar-100 and TinylmageNet datasets, we demonstrate that the proposed method outperforms the other KD methods and analyze feature representation to show its effectiveness in the framework of transfer learning.