Knowledge Distillation via Token-level Relationship Graph

• URL: http://arxiv.org/abs/2306.12442v1
• Date: Tue, 20 Jun 2023 08:16:37 GMT
• Title: Knowledge Distillation via Token-level Relationship Graph
• Authors: Shuoxi Zhang, Hanpeng Liu, Kun He
• Abstract summary: We propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches.
• Score: 12.356770685214498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation is a powerful technique for transferring knowledge from a pre-trained teacher model to a student model. However, the true potential of knowledge transfer has not been fully explored. Existing approaches primarily focus on distilling individual information or instance-level relationships, overlooking the valuable information embedded in token-level relationships, which may be particularly affected by the long-tail effects. To address the above limitations, we propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) that leverages the token-wise relational knowledge to enhance the performance of knowledge distillation. By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model, resulting in improved distillation results. To further enhance the learning process, we introduce a token-wise contextual loss called contextual loss, which encourages the student model to capture the inner-instance semantic contextual of the teacher model. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches. Empirical results demonstrate the superiority of TRG across various visual classification tasks, including those involving imbalanced data. Our method consistently outperforms the existing baselines, establishing a new state-of-the-art performance in the field of knowledge distillation.

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