Domain adaptation tackles the challenge of generalizing knowledge acquired from a source domain to a target domain with different data distributions. Traditional domain adaptation methods presume that the classes in the source and target domains are identical, which is not always the case in real-world scenarios. Open-set domain adaptation (OSDA) addresses this limitation by allowing previously unseen classes in the target domain. Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples. We propose a novel framework based on self-paced learning to distinguish common and unknown class samples precisely, referred to as SPLOS (self-paced learning for open-set). To utilize unlabeled target samples for self-paced learning, we generate pseudo labels and design a cross-domain mixup method tailored for OSDA scenarios. This strategy minimizes the noise from pseudo labels and ensures our model progressively learns common class features of the target domain, beginning with simpler examples and advancing to more complex ones. Furthermore, unlike existing OSDA methods that require manual hyperparameter $threshold$ tuning to separate common and unknown classes, our approach self-tunes a suitable threshold, eliminating the need for empirical tuning during testing. Comprehensive experiments illustrate that our method consistently achieves superior performance on different benchmarks compared with various state-of-the-art methods.

翻译：域适应解决了将从源域获取的知识泛化到具有不同数据分布的目标域的挑战。传统域适应方法假设源域和目标域中的类别完全相同，但这在现实场景中并非总是成立。开放集域适应（OSDA）通过允许目标域中出现未见过的类别来克服这一限制。开放集域适应不仅旨在识别属于源域和目标域共享的共同类别的目标样本，还需感知未知类别的样本。我们提出了一种基于自步学习的新型框架，用于精确区分共同类别和未知类别的样本，该框架称为SPLOS（开放集的自步学习）。为了利用未标记的目标样本进行自步学习，我们生成伪标签，并设计了一种针对OSDA场景定制的跨域混合方法。该策略最小化了伪标签的噪声，确保我们的模型从简单示例逐步过渡到复杂示例，渐进式地学习目标域的共同类别特征。此外，与现有需要手动调整超参数$threshold$以分离共同类和未知类的OSDA方法不同，我们的方法能够自动调整合适的阈值，无需在测试阶段进行经验调参。综合实验表明，与多种最先进的方法相比，我们的方法在不同基准测试上始终能够实现更优的性能。