  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.
