  Knowledge distillation is to transfer the knowledge from the data learned by
the teacher network to the student network, so that the student has the
advantage of less parameters and less calculations, and the accuracy is close
to the teacher. In this paper, we propose a new distillation method, which
contains two transfer distillation strategies and a loss decay strategy. The
first transfer strategy is based on channel-wise attention, called Channel
Distillation (CD). CD transfers the channel information from the teacher to the
student. The second is Guided Knowledge Distillation (GKD). Unlike Knowledge
Distillation (KD), which allows the student to mimic each sample's prediction
distribution of the teacher, GKD only enables the student to mimic the correct
output of the teacher. The last part is Early Decay Teacher (EDT). During the
training process, we gradually decay the weight of the distillation loss. The
purpose is to enable the student to gradually control the optimization rather
than the teacher. Our proposed method is evaluated on ImageNet and CIFAR100. On
ImageNet, we achieve 27.68% of top-1 error with ResNet18, which outperforms
state-of-the-art methods. On CIFAR100, we achieve surprising result that the
student outperforms the teacher. Code is available at
https://github.com/zhouzaida/channel-distillation.
