Unverified50% confidenceFactExact time
可借助Fréchet Inception Distance（FID）或Maximum Mean Discrepancy（MMD）等分布距离度量来量化增强后训练分布与真实测试分布之间的偏移
1
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50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
Unverified量化测量域偏移程度的常用方法包括最大均值差异（MMD）和Fréchet Inception Distance（FID）80% similarUnverifiedFID（Fréchet Inception Distance）由Heusel等人于2017年提出，用预训练Inception网络提取特征并将分布建模为多元高斯，计算Fréchet距离，FID值越低表明分布越接近79% similarUnverifiedFID (Fréchet Inception Distance) evaluates generation quality by comparing the statistical distribution differences between real and generated data in the feature space of a deep neural network, with lower values indicating closer resemblance to real data.74% similarUnverified分布偏移意味着训练集与测试集之间的KL散度不为零，即两个分布之间存在可度量的信息差距70% similar
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