Teacher-student collaborative knowledge distillation for image classification
Applied Intelligence, vol. 53, pp. 1997–2009
Abstract
A single model usually cannot learn all the appropriate features with limited data, thus leading to poor performance when test data are used. To improve model performance, we propose a teacher-student collaborative knowledge distillation (TSKD) method based on knowledge distillation and self-distillation. The method consists of two parts: learning in the teacher network and self-teaching in the student network. Learning in the teacher network allows the student network to use knowledge from the teacher network. Self-teaching in the student network is to build a multi-exit network based on self-distillation and provide deep features as supervised information for training. In the inference stage, we use ensembles to vote on the classification results of multiple sub-models in the student network. The experimental results demonstrate the superior performance of our method compared with a traditional knowledge distillation method and a self-distillation-based multi-exit network.
Authors 6
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Chongqing Normal University · Chongqing University of Technology
Affiliation as printed
College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China
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Wenjian Gao corresponding
Chongqing University of Technology
Affiliation as printed
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China
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Affiliation as printed
Computer Science Department, RWTH Aachen University, Aachen, 52074, Germany
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Chongqing University of Technology
Affiliation as printed
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China
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Chongqing University of Technology
Affiliation as printed
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 400054, China
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Affiliation as printed
College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China
Cited by 71 stored of 71
Cited by patents worldwide 1 (Lens.org)
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