  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.
