Out-of-Distribution (OOD) detection is essential in real-world applications,
which has attracted increasing attention in recent years. However, most
existing OOD detection methods require many labeled In-Distribution (ID) data,
causing a heavy labeling cost. In this paper, we focus on the more realistic
scenario, where limited labeled data and abundant unlabeled data are available,
and these unlabeled data are mixed with ID and OOD samples. We propose the
Adaptive In-Out-aware Learning (AIOL) method, in which we employ the
appropriate temperature to adaptively select potential ID and OOD samples from
the mixed unlabeled data and consider the entropy over them for OOD detection.
Moreover, since the test data in realistic applications may contain OOD samples
whose classes are not in the mixed unlabeled data (we call them unseen OOD
classes), data augmentation techniques are brought into the method to further
improve the performance. The experiments are conducted on various benchmark
datasets, which demonstrate the superiority of our method.

本研究主要解决有限标记数据和丰富无标记数据可用的更加现实的情况下的 OOD 检测问题。我们提出了一种自适应的基于样本熵和适当温度选择潜在 ID 和 OOD 样本的方法 (AIOL)，并利用数据扩充技术进一步提高了其性能。我们在多种基准数据集上进行的实验表明，我们的方法优于其他方法。