Clean Sample Guided Self-Knowledge Distillation for Image Classification

For two-stage knowledge distillation, the combination with Data Augmentation (DA) is straightforward and effective. Yet, for online Self-knowledge Distillation (SD), DA is not always beneficial because of the absence of a trustworthy teacher model. To address this issue, this paper proposes an SD method named Clean sample guided Self-knowledge Distillation (CleanSD), in which the original clean sample is used as a guide when the model is trained with the augmented samples. The implementation of the CleanSD comes with two DA techniques, namely Mixup (for label-mixing) and Cutout (for label-preserving). Results on CIFAR-100 demonstrate that error rates obtained by the proposed CleanSD are reduced by 2.59%, 1.39%, and 0.47-1.20%, compared to that obtained by the baseline, the vanilla DA techniques, and other peer SD methods, respectively. In addition, the effectiveness and robustness of the CleanSD are verified across multiple DA methods and datasets.