Open Set Domain Adaptation (OSDA) focuses on bridging the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source as unknown. The challenges of this task are closely related to those of Positive-Unlabeled (PU) learning where it is essential to discriminate between positive (known) and negative (unknown) class samples in the unlabeled target data. With this newly discovered connection, we leverage the theoretical framework of PU learning for OSDA and, at the same time, we extend PU learning to tackle uneven data distributions. Our method combines domain adversarial learning with a new non-negative risk estimator for PU learning based on self-supervised sample reconstruction. With experiments on digit recognition and object classification, we validate our risk estimator and demonstrate that our approach allows reducing the domain gap without suffering from negative transfer.

开放集域适配（OSDA）致力于弥合标记源域和未标记目标域之间的域差距，同时也拒绝源中不存在的目标类（未知）。这项任务的挑战与正未标记（PU）学习的挑战紧密相关，在此学习中，必须区分未标记目标数据中的正（已知）和负（未知）类别样本。通过这一新发现的联系，我们将PU学习的理论框架用于OSDA，同时，我们扩展了PU学习以解决数据分布不均的问题。我们的方法将领域对抗性学习与基于自监督样本重构的用于PU学习的新的非负风险估计器相结合。通过数字识别和对象分类的实验，