In this paper, we investigate $\textit{open-set recognition}$ with domain shift, where the final goal is to achieve $\textit{Source-free Universal Domain Adaptation}$ (SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. 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: $\textbf{1)}$ After source training, the resulting source model can get excellent performance for $\textit{open-set single domain generalization}$ and also $\textit{open-set recognition}$ tasks; $\textbf{2)}$ After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation on several benchmarks. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method. $\textbf{3)}$ When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively. Code will be available in https://github.com/Albert0147/OneRing.
翻译:在本文中, 我们调查$\ textit{ 开放式识别 $ $ textit{ 开放式识别 $ 域变换, 最终目标是实现$\ textit{ 不含源的源类别调整} $( SF- UNDA), 解决源域和目标域间存在域和类别变化的情况。 在 SF- UNDA 设置下, 模型在目标调整期间无法再访问源数据, 目的是解决数据隐私问题。 我们提出一个新的培训计划, 学习一个 $+1 路分级, 以预测美元源类和未知类, 只有已知来源类别的样本可供培训使用。 此外, 为了目标调整, 我们简单地采用加权最小最小化最小化, 使源模式适应无源域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域内, 内域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域域