May 15, 2018 · In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier.
To realize this goal, we utilize an adversarial attack to dis- cover samples supporting a decision boundary. Based on this idea, to transfer more accurate ...
Official Pytorch implementation of paper: Knowledge Distillation with Adversarial Samples Supporting Decision Boundary (AAAI 2019).
The proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary.
Jan 24, 2019 · In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The ...
In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier. The generalization ...
Jul 7, 2023 · In this paper, we aim to reveal the integrated relationship between a given DL model and the corresponding training dataset, by framing the problem of ...
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May 2, 2021 · We select adversarial samples which are close to the decision boundary of two classes to metric the distance with negative class samples ...
Our method is compared to KD +. BSS, which uses adversarial examples to support student decision boundaries. We calculate MagSim and AngSim, as shown in Fig. 6.
Knowledge Distillation with Adversarial Samples Supporting ...
pod3275.github.io › paper › 2019/08/02
Aug 2, 2019 · 이 논문에서는 더욱 효과적인 Knowledge Distillation 방법을 제안함. 모델의 결정 경계 (decision boundary) 근처에 있는 데이터를 이용. 특히, ...