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In this paper, we investigate Source-free Open-partial Domain Adaptation(SF-OPDA), which addresses the situation where there exist both domain andcategory shifts between source and target domains. Under the SF-OPDA setting,which aims to address data privacy concerns, the model cannot access sourcedata anymore during target adaptation. We propose a novel training scheme tolearn a (n+1)-way classifier to predict the n source classes and the unknownclass, where samples of only known source categories are available fortraining. Furthermore, for target adaptation, we simply adopt a weightedentropy minimization to adapt the source pretrained model to the unlabeledtarget domain without source data. In experiments, we show our simple methodsurpasses current OPDA approaches which demand source data during adaptation.When augmented with a closed-set domain adaptation approach during targetadaptation, our source-free method further outperforms the currentstate-of-the-art OPDA method by 2.5%, 7.2% and 13% on Office-31, Office-Homeand VisDA respectively.

OneRing: A Simple Method for Source-free Open-partial Domain Adaptation