… to compress large networks into more resource-efficient ones with minimal accuracy loss. In
… We presented in this paper a novel distillation loss for capturing and transferring knowledge …

… misalignment between KD loss and classification loss would be severer, thus disturbing the
student’s training. As a result, the exact match (ie, the loss reaches the minimal if and only if …

… generalizable solution by weighted averaging the local minimum located in the border of
areas with lower loss. Considering the relationship between the loss landscape’s geometry and …

… knowledge distillation approach which does not suffer from these drawbacks. The idea behind
knowledge distillation is to “distill” … We closely follow the original distillation setup from [12] …

… Besides the visualization of optimization trajectory, we also observe that the new local
minimum has better generalization capacity and is more robust to random noise in input space. …

… adapter module [19] for knowledge distillation with updating parameters of this adapter …
knowledge distillation methods for comparison, including Vanilla [1], knowledge distillation …

… Loss function To improve the generalization performance, we distill the knowledge of soft …
Then a neat way to generate teacher logit is to select the minimum element of each row of …

… in knowledge distillation and a surge of related literature on designing better distillation …
this problem, but few minimal efforts have been put into understanding this phenomenon. …

… In this work, we propose a Knowledge distillation that allows transferring the knowledge of
the large SNN to smaller one in a disciplined fashion with minimal loss in performance. We …

… Knowledge distillation was utilized in this paper to propose a method for bridging the gap …
Despite its simplicity, the student model receives the dark knowledge of multi-lead ECG signals …