Before revision:  In contrast to existing kNN augmentation techniques that blindly incorporate all samples, our method dynamically selects a subset of augmented samples with respect to the maximum KL-divergence of the training loss .
After revision:  In contrast to existing kNN augmentation techniques that blindly incorporate all samples, our method dynamically selects a subset of augmented samples with respect to the maximum KL-divergence between the teacher and student models .