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Updated: Jun 18, 2025

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分离图形知识蒸:一种基于logits的通用方法,用于在图形上学习MLP.

Yingjie Tian1, Shaokai Xu2, Muyang Li2

  • 1School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences, Beijing, 100190, China.

Neural networks : the official journal of the International Neural Network Society
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PubMed
概括
此摘要是机器生成的。

解图知识蒸 (DGKD) 通过解蒸损失来改善多层感知子 (MLP). 这种方法通过灵活调整目标和非目标类蒸重量来提高学生模型的准确性.

关键词:
脱是脱的过程.图表知识的蒸.图形神经网络是一个神经网络.多层感知子是多层的感知子

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科学领域:

  • 机器学习 机器学习
  • 图形神经网络的神经网络
  • 人工智能的人工智能

背景情况:

  • 图形神经网络 (GNN) 在非欧几里德数据方面表现出色,但在实时应用中计算密集.
  • 图形知识蒸 (KD) 训练高效的多层感知子 (MLP) 来取代GNN.
  • 当前的KD方法往往忽略了逻辑层蒸,专注于中间特征.

研究的目的:

  • 引入一种新的基于逻辑的图形知识蒸方法.
  • 为了解决现有的图形KD方法的局限性,重点关注logit层.
  • 提高学生MLP在基于图表的任务中的效率和准确性.

主要方法:

  • 介绍了脱图知识蒸 (DGKD),专注于逻辑层蒸.
  • 将KD损失重组为目标类图形蒸 (TCGD) 和非目标类图形蒸 (NCGD) 的损失.
  • 分离了预测信心和NCGD之间的负相关性,并消除了TCGD和NCGD之间的固定权重.

主要成果:

  • 在基准数据集中,DGKD证明了学生MLP的预测准确度有所提高.
  • 与现有的图形KD技术相比,该方法实现了更高的性能.
  • 作为增强其他KD框架的plug-and-play组件,DGKD展示了灵活性.

结论:

  • DGKD提供了一种有效的基于逻辑的方法来对图形知识进行蒸.
  • 分离策略通过优化蒸损失组件来提高学生的MLP表现.
  • 在工业环境中,DGKD为部署高效的GNN类模型提供了一个有价值和可适应的工具.