Complementary Relation Contrastive Distillation

• URL: http://arxiv.org/abs/2103.16367v1
• Date: Mon, 29 Mar 2021 02:43:03 GMT
• Title: Complementary Relation Contrastive Distillation
• Authors: Jinguo Zhu and Shixiang Tang and Dapeng Chen and Shijie Yu and Yakun Liu and Aijun Yang and Mingzhe Rong and Xiaohua Wang
• Abstract summary: We propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD) We estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD.
• Score: 13.944372633594085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled in a more effective way. In this paper, we propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD), to transfer the structural knowledge from the teacher to the student. Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. To make it more robust, mutual relations are modeled by two complementary elements: the feature and its gradient. Furthermore, the low bound of mutual information between the anchor-teacher relation distribution and the anchor-student relation distribution is maximized via relation contrastive loss, which can distill both the sample representation and the inter-sample relations. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD.

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