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Knowledge distillation is one of the methods for model compression, andexisting knowledge distillation techniques focus on how to improve thedistillation algorithm so as to enhance the distillation efficiency. This paperintroduces dynamic incremental learning into knowledge distillation andproposes a distillation strategy for education distillation. Specifically, itis proposed to take fragmented student models divided from the complete studentmodel as lower-grade models. As the grade level rises, fragmented studentmodels deepen in conjunction with designed teaching reference layers, whilelearning and distilling from more teacher models. By moving from lower tohigher grades, fragmented student models were gradually integrated into acomplete target student model, and the performance of the student modelsgradually improved from lower to higher grades of the stage. Educationdistillation strategies combined with distillation algorithms outperform theresults of single distillation algorithms on the public datasetCIFAR100,Caltech256, Food-101 dataset.

Education distillation:getting student models to learn in shcools