Scale Decoupled Distillation

TL;DR

This paper introduces Scale Decoupled Distillation (SDD), a novel logit knowledge distillation method that decouples global logits into local parts to transfer more precise semantic knowledge and improve student model performance, especially in fine-grained tasks.

Contribution

The paper proposes SDD, which decouples logits into local components and separates consistent and complementary knowledge, enhancing the effectiveness of logit-based knowledge distillation.

Findings

SDD outperforms existing methods on benchmark datasets.

Decoupling logits improves transfer of fine-grained semantic knowledge.

Focusing on complementary knowledge enhances discrimination of ambiguous samples.

Abstract

Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However, it often suffers inferior performance compared to the feature knowledge distillation. In this paper, we argue that existing logit-based methods may be sub-optimal since they only leverage the global logit output that couples multiple semantic knowledge. This may transfer ambiguous knowledge to the student and mislead its learning. To this end, we propose a simple but effective method, i.e., Scale Decoupled Distillation (SDD), for logit knowledge distillation. SDD decouples the global logit output into multiple local logit outputs and establishes distillation pipelines for them. This helps the student to mine and inherit fine-grained and unambiguous logit knowledge. Moreover, the decoupled knowledge can be further divided into consistent and complementary logit knowledge that transfers the semantic information and sample ambiguity, respectively. By increasing the weight of complementary parts, SDD can guide the student to focus more on ambiguous samples, improving its discrimination ability. Extensive experiments on several benchmark datasets demonstrate the effectiveness of SDD for wide teacher-student pairs, especially in the fine-grained classification task. Code is available at: https://github.com/shicaiwei123/SDD-CVPR2024