  We ask the following question: what training information is required to
design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting
samples that lie far away from the training distribution? Since unlabeled data
is easily accessible for many applications, the most compelling approach is to
develop detectors based on only unlabeled in-distribution data. However, we
observe that most existing detectors based on unlabeled data perform poorly,
often equivalent to a random prediction. In contrast, existing state-of-the-art
OOD detectors achieve impressive performance but require access to fine-grained
data labels for supervised training. We propose SSD, an outlier detector based
on only unlabeled in-distribution data. We use self-supervised representation
learning followed by a Mahalanobis distance based detection in the feature
space. We demonstrate that SSD outperforms most existing detectors based on
unlabeled data by a large margin. Additionally, SSD even achieves performance
on par, and sometimes even better, with supervised training based detectors.
Finally, we expand our detection framework with two key extensions. First, we
formulate few-shot OOD detection, in which the detector has access to only one
to five samples from each class of the targeted OOD dataset. Second, we extend
our framework to incorporate training data labels, if available. We find that
our novel detection framework based on SSD displays enhanced performance with
these extensions, and achieves state-of-the-art performance. Our code is
publicly available at https://github.com/inspire-group/SSD.
