深度学习
机器学习
卷积神经网络
模式识别(心理学)
多任务学习
特征学习
无监督学习
作者
Ying Zhang,Tao Xiang,Timothy M. Hospedales,Huchuan Lu
出处
期刊:Computer Vision and Pattern Recognition
日期:2018-06-01
被引量:1010
标识
DOI:10.1109/cvpr.2018.00454
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI