Enhancing Logits Distillation with Plug&Play Kendall's $\tau$ Ranking Loss
Keywords: Knowledge Distillation, Kendall's tau Coefficient, Ranking Loss
TL;DR: Plug & Play Ranking Loss for Logits Distillation
Abstract: Knowledge distillation typically employs the Kullback-Leibler (KL) divergence to constrain the output of the student model to precisely match the soft labels provided by the teacher model. However, the optimization process of KL divergence is challenging for the student and prone to suboptimal points. Also, we demonstrate that the gradients provided by KL divergence depend on channel scale and thus tend to overlook low-probability channels. The mismatch in low-probability channels also results in the neglect of inter-class relationship information, making it difficult for the student to further enhance performance. To address this issue, we propose an auxiliary ranking loss based on Kendall’s $\tau$ Coefficient, which can be plug-and-play in any logit-based distillation method, providing inter-class relationship information and balancing the attention to low-probability channels. We show that the proposed ranking loss is less affected by channel scale, and its optimization objective is consistent with that of KL divergence. Extensive experiments on CIFAR-100, ImageNet, and COCO datasets, as well as various CNN and ViT teacher-student architecture combinations, demonstrate that the proposed ranking loss can be plug-and-play on various baselines and enhance their performance.
Supplementary Material: zip
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 10857
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