Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually more time-consuming and costly than unlabeled data. When ID labeled data is limited, the previous OOD detection methods are no longer superior due to their high dependence on the amount of ID labeled data. Based on limited ID labeled data and sufficient unlabeled data, we define a new setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To solve the new problem, we propose an effective method called Topological Structure Learning (TSL). Firstly, TSL uses a contrastive learning method to build the initial topological structure space for ID and OOD data. Secondly, TSL mines effective topological connections in the initial topological space. Finally, based on limited ID labeled data and mined topological connections, TSL reconstructs the topological structure in a new topological space to increase the separability of ID and OOD instances. Extensive studies on several representative datasets show that TSL remarkably outperforms the state-of-the-art, verifying the validity and robustness of our method in the new setting of WSOOD.

翻译：在开放的世界中安全部署模型的关键是外部分配(OOD)检测。对于 OOD 检测,收集足够的分布(ID)标签数据通常比未贴标签的数据更费时、费用更高。当ID标签数据有限时,以前的OOD检测方法不再优越,因为它们高度依赖ID标签数据的数量。根据有限的标识数据和足够的未贴标签数据,我们定义了一个新的环境,称为WSOOD。为了解决新的问题,我们提出了一种有效的方法,称为地形结构学习(TSL)。首先,TRSL使用对比性学习方法为ID和OOD数据建立初始的地形结构空间。第二,TRS在初始表面空间中有效挖掘地表层连接。最后,根据有限的标识数据和埋设的地形连接,TSLF在一个新的地形空间中重建地形结构结构,以增加身份识别和 OOD 实例的可分性。关于若干具有代表性的数据设置的广泛研究显示,TSLOD的可靠性,并证明WOD方法的可靠性。