Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large network or ensemble to a small network, in order to meet the low-memory or fast execution requirements. In this paper, we present a deep mutual learning (DML) strategy. Different from the one-way transfer between a static pre-defined teacher and a student in model distillation, with DML, an ensemble of students learn collaboratively and teach each other throughout the training process. Our experiments show that a variety of network architectures benefit from mutual learning and achieve compelling results on both category and instance recognition tasks. Surprisingly, it is revealed that no prior powerful teacher network is necessary -mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher. It is perhaps not obvious why the proposed learning strategy should work at all. Where does the additional knowledge come from, when the learning process starts out with
Moreover, Zhang et al. propose deep mutual learning (DML) strategy, where two networks learn collaboratively and teach each other REF .
Deep Mutual Learning
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science