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Mixup-based maximum distribution difference selection strategy for domain generalization

delete2025-07-01
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PRE
AI
Y
Yuqi Wang
Z
Zhanshan Li
余海红 封面图
余海红 (Haihong Yu)
李静瑶 封面图
李静瑶 (Jingyao Li) *
DOI：10.1016/j.eswa.2025.127521delete
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摘要

摘要

En 中文
领域泛化是迁移学习问题的一个分支，其中源域和目标域分布不同，目标是将训练于源域的模型推广到未见的目标域。一种有前景的解决方案是应用mixup生成新特征，但现有方法通常直接从不同域中选择两个样本进行mixup。我们发现这种跨域选择方法存在需要域标签、在模型具有多个mixup时失效等缺点。为此，本文设计了一种新的样本选择算法，用于选择分布差异大的样本对进行mixup。此外，我们提出了间接多样本mixup方法，以更大规模地探索未见目标域的分布。为解决训练后期原始数据引起的过拟合问题，我们设计了调整mix比率的公式，从而随着训练进程逐步增加原始数据的mix比率。最后，在四个公开基准数据集PACS、OfficeHome、VLCS和TerraIncognita上的大量实验表明，与领域泛化领域的最新方法相比，我们的方法表现更优。
Keyword:
Domain generalization
Transfer learning
Maximum Mean Discrepancy

期刊

Expert Systems with Applications 封面图
Expert Systems with Applications
IF:
7.5
论文数:
2.9W
被引数:
10.2W

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