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구분 SCI
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학술대회 A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
Cited 44 time in scopus
저자
임준호, 주동규, 배지훈, 김준모
발행일
201707
출처
Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pp.7130-7138
DOI
https://dx.doi.org/10.1109/CVPR.2017.754
협약과제
16AU2800, 자가학습형 지식융합 슈퍼브레인 핵심기술개발, 표철식
초록
© 2017 IEEE. We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN maps from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model; (2) the student DNN outperforms the original DNN; and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.
KSP 제안 키워드
Deep neural network(DNN), Fast optimization, Inner Product, Knowledge transfer, Network minimization, Novel technique, Transfer learning, knowledge distillation, two layers