Open-set domain adaptation (OSDA) aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples. Existing OSDA methods suffer from two obstacles. First, a tedious process of manually tuning a hyperparameter $threshold$ is required for most OSDA approaches to separate common and unknown classes. It is difficult to determine a proper threshold when the target domain data is unlabeled. Second, most OSDA methods only rely on confidence values predicted by models to distinguish common/unknown classes. The performance is not satisfied, especially when the majority of the target domain consists of unknown classes. Our experiments demonstrate that combining entropy, consistency, and confidence is a more reliable way of distinguishing common and unknown samples. In this paper, we design a novel threshold self-tuning and cross-domain mixup (TSCM) method to overcome the two drawbacks. TSCM can automatically tune a proper threshold utilizing unlabeled target samples rather than manually setting an empirical hyperparameter. Our method considers multiple criteria instead of only the confidence and uses the threshold generated by itself to separate common and unknown classes in the target domain. Furthermore, we introduce a cross-domain mixup method designed for OSDA scenarios to learn domain-invariant features in a more continuous latent space. Comprehensive experiments illustrate that our method consistently achieves superior performance on different benchmarks compared with various state-of-the-arts.

翻译：开放集域适应（OSDA）旨在不仅识别属于源域和目标域共享共同类别的目标样本，还能感知未知类别的样本。现有OSDA方法面临两大障碍：首先，多数OSDA方法需要繁琐的手动调整超参数阈值来分离共同类别与未知类别，当目标域数据无标签时难以确定合适的阈值；其次，多数OSDA方法仅依赖模型预测的置信度来区分共同/未知类别，当目标域中未知类别占多数时性能欠佳。我们实验证明，结合熵、一致性和置信度是区分共同与未知样本的更可靠方式。本文提出一种新颖的阈值自适应与跨域混合（TSCM）方法克服上述缺陷：TSCM可利用无标签目标样本自动调节合适阈值，无需手动设置经验超参数；该方法同时考虑多重准则而非仅依赖置信度，并利用自生成阈值分离目标域中的共同与未知类别。此外，我们引入专为OSDA场景设计的跨域混合方法，在更连续的潜在空间中学习域不变特征。综合实验表明，与多种最先进方法相比，本方法在不同基准测试中持续取得更优性能。