Self-Knowledge Distillation (Self-KD), a technique that enables neural networks to learn from themselves, often relies on auxiliary modules or networks to ...
Jul 21, 2023 · Self-Knowledge distillation, a technique that enables neural networks to learn from themselves, often relies on auxiliary modules or networks to ...
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Neighbor Self-Knowledge Distillation. https://doi.org/10.2139/ssrn.4517471. Journal: 2023. Publisher: Elsevier BV. Authors: Peng Liang, Weiwei Zhang, Junhuang ...
Mar 6, 2024 · We propose a Teacher-Free Graph Self-Distillation (TGS) framework that does not require any teacher model or GNNs during both training and inference.
Nearest Neighbor Knowledge Distillation for Neural Machine ...
aclanthology.org › 2022.naacl-main.406
Distilling knowledge retrieved by kNN can encourage the NMT model to take more reasonable target tokens into consideration, thus addressing the overcorrection ...
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This paper introduces a novel model for image classification which can be applied to object detection, namely, self-distillation and k-nearest neighbor-based ...
This results in regularizing the dark knowledge (i.e., the knowledge on wrong predictions) of a single network (i.e., a self-knowledge distillation) by forcing ...
Nov 4, 2020 · In this paper, we propose the first teacher-free knowledge distillation method for GNNs, termed GNN Self-Distillation (GNN-SD), that serves as a drop-in ...
The proposed Graph Self-Distillation on Neighborhood (GSDN) framework is based purely on MLPs, where structural information is only implicitly used as prior ...
The paper presents a novel method called Neighbor Exitwise Orthogonal Knowledge Distillation (NEO-KD) for improving the adversarial robustness of multi-exit ...