Comments (3)
您好,在不同的机器(GPU/CPU等)上结果会有波动,您可以尝试下不同的随机种子,或者在当前种子下微调lr、dropout、norm、L和K值。我们也给出了我们训练的几个checkpoints。
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您好,我对您的这篇论文非常感兴趣!
@inproceedings{chen2023multivariate,
title={Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation},
author={Chen, Feiyu and Shao, Jie and Zhu, Shuyuan and Shen, Heng Tao},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10761--10770},
year={2023}
}
您在该论文中,所提出的将基于超图的多元传播和基于普通图的多频传播的双通道并行理念让我耳目一新。
但是,针对您给出的代码,我有几点疑惑
-
weight:在model_hyper.py的155行中,您定义了一个weight,其用于HypergraphConv.py中计算节点的度B
-
EW_weight:在model_hyper.py的156行中,您定义了一个EW_weight,其用于HypergraphConv.py中计算超边的度D
并且我在后续中未找到调整这些参数的代码部分。如果权重没有调整,那超边的构建方法是否过于简单
我是一个ERC领域的初学者,如果能得到您的点拨,我将不胜感激!
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两个weight都会更新,抱歉,我的问题!
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