论文标题
Onering：一种无源开放式域改编的简单方法
OneRing: A Simple Method for Source-free Open-partial Domain Adaptation
论文作者
论文摘要
在本文中，我们研究了无源开放式域的适应（SF-OPDA），该域（SF-OPDA）解决了源和目标域之间存在域和类别变化的情况。在旨在解决数据隐私问题的SF-OPDA设置下，该模型在目标适应过程中无法再访问源数据。我们提出了一种新颖的培训计划，以学习（n+1） - 道路分类器来预测n个源类和未知类别，其中仅可用于培训的样本。此外，对于目标适应，我们简单地采用了加权熵最小化，以使源预处理的模型适应没有标记的目标域而没有源数据。在实验中，我们显示我们的简单方法超过了当前的OPDA方法，这些方法在适应过程中需要源数据。当目标适应过程中使用封闭域的适应方法增强时，我们的无源方法进一步超过了当前的最新OPDA方法，分别在Office-31，Office-Home和Visda上分别以2.5％，7.2％和13％的速度。
In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA setting, which aims to address data privacy concerns, the model cannot access source data anymore during target adaptation. We propose a novel training scheme to learn a (n+1)-way classifier to predict the n source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show our simple method surpasses current OPDA approaches which demand source data during adaptation. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art OPDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
