NormKD: Normalized Logits for Knowledge Distillation

TL;DR

NormKD introduces a sample-specific temperature adjustment in logit-based knowledge distillation, significantly improving performance on image classification tasks without extra computational costs.

Contribution

The paper proposes Normalized Knowledge Distillation (NormKD), a novel method that customizes the temperature for each sample based on its logit distribution, enhancing distillation effectiveness.

Findings

Significantly better performance on CIRAR-100 and ImageNet.

Comparable or superior results to feature-based methods.

No extra computational or storage costs.

Abstract

Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potential when we re-investigate the temperature, which is a crucial hyper-parameter to soften the logit outputs. For most of the previous works, it was set as a fixed value for the entire distillation procedure. However, as the logits from different samples are distributed quite variously, it is not feasible to soften all of them to an equal degree by just a single temperature, which may make the previous work transfer the knowledge of each sample inadequately. In this paper, we restudy the hyper-parameter temperature and figure out its incapability to distill the knowledge from each sample sufficiently when it is a single value. To address this issue, we propose Normalized Knowledge Distillation (NormKD), with the purpose of customizing the temperature for each sample according to the characteristic of the sample's logit distribution. Compared to the vanilla KD, NormKD barely has extra computation or storage cost but performs significantly better on CIRAR-100 and ImageNet for image classification. Furthermore, NormKD can be easily applied to the other logit based methods and achieve better performance which can be closer to or even better than the feature based method.