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Aug, 2022
基于不确定性引导的无源域自适应
Uncertainty-guided Source-free Domain Adaptation
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Subhankar Roy, Martin Trapp, Andrea Pilzer, Juho Kannala, Nicu Sebe...
TL;DR
该研究提出了一种基于概率模型的源自由领域自适应方法,通过量化源模型预测中的不确定性来指导目标适应,以对抗域漂移和缺乏源数据的问题,并且相比于传统的源自由领域自适应方法,该方法计算轻量、与源训练和目标适应相独立。
Abstract
source-free domain adaptation
(SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the
domain shift
makes the pr
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