Student Network Learning via Evolutionary Knowledge Distillation

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 4; pp. 2251 - 2263
• Main Authors: Zhang, Kangkai, Zhang, Chunhui, Li, Shikun, Zeng, Dan, Ge, Shiming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data mining; Data models; deep learning; Distillation; Evolution; Germanium; Knowledge; Knowledge distillation; Knowledge management; Knowledge representation; Knowledge transfer; Learning; Predictive models; Teachers; teacher–student learning; Training
• DOI: 10.1109/TCSVT.2021.3090902
• ISSN: 1051-8215

Knowledge distillation provides an effective way to transfer knowledge via teacher-student learning, where most existing distillation approaches apply a fixed pre-trained model as teacher to supervise the learning of student network. This manner usually brings in a big capability gap between teacher and student networks during learning. Recent researches have observed that a small teacher-student capability gap can facilitate knowledge transfer. Inspired by that, we propose an evolutionary knowledge distillation approach to improve the transfer effectiveness of teacher knowledge. Instead of a fixed pre-trained teacher, an evolutionary teacher is learned online and consistently transfers intermediate knowledge to supervise student network learning on-the-fly. To enhance intermediate knowledge representation and mimicking, several simple guided modules are introduced between corresponding teacher-student blocks. In this way, the student can simultaneously obtain rich internal knowledge and capture its growth process, leading to effective student network learning. Extensive experiments clearly demonstrate the effectiveness of our approach as well as good adaptability in the low-resolution and few-sample scenarios.