相关实验视频
Updated: Jun 2, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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一种通用策略,用于平滑深图神经网络中的减速
概括
我们引入了平滑减速 (SD) 策略,以打击深度图形神经网络 (GNN) 的过度平滑. 这种方法减少了光滑速度,通过保留类内信息和改进剩余计算来提高性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
背景情况:
- 图形神经网络 (GNN) 擅长模拟图形数据,但在深层中遭受过度平滑.
- 现有的方法往往过于简化了平滑,忽视了类内利益和邻居分布.
研究的目的:
- 提出一种新的平滑减速 (SD) 策略,以减轻深度GNN中的过度平滑.
- 通过解决当前光滑减少技术的局限性来提高GNN性能.
主要方法:
- 分析了使用微分运算的节点表示光滑速度.
- 引入了与类相关的光滑减速 (CR-SD) 损失,以平衡类间和类内光滑.
- 开发了光滑减速余量 (NAR) 以考虑邻近分布进行高效的余量计算.
主要成果:
- SD 策略有效地降低了深度 GNN 中的平滑速度.
- CR-SD损失保留了有益的类内平滑,同时减少了有害的类间平滑.
- NAR模块通过结合邻近节点分布来改进剩余重量计算.
- 实验结果显示,SD增强的GNN的性能优于基线和现有的深度GNN模型.
结论:
- 拟议的SD战略成功地将浅层GNN扩展到更深层架构.
- SD策略有效地减轻了过度平滑,从而提高了深度GNN的性能.
- 新的CR-SD损失和NAR模块在GNN设计中提供了显著的进步.
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