  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.
