  Handling out-of-distribution (OOD) samples has become a major stake in the
real-world deployment of machine learning systems. This work explores the use
of self-supervised contrastive learning to the simultaneous detection of two
types of OOD samples: unseen classes and adversarial perturbations. First, we
pair self-supervised contrastive learning with the maximum mean discrepancy
(MMD) two-sample test. This approach enables us to robustly test whether two
independent sets of samples originate from the same distribution, and we
demonstrate its effectiveness by discriminating between CIFAR-10 and CIFAR-10.1
with higher confidence than previous work. Motivated by this success, we
introduce CADet (Contrastive Anomaly Detection), a novel method for OOD
detection of single samples. CADet draws inspiration from MMD, but leverages
the similarity between contrastive transformations of a same sample. CADet
outperforms existing adversarial detection methods in identifying adversarially
perturbed samples on ImageNet and achieves comparable performance to unseen
label detection methods on two challenging benchmarks: ImageNet-O and
iNaturalist. Significantly, CADet is fully self-supervised and requires neither
labels for in-distribution samples nor access to OOD examples.
