  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
