CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning

• URL: http://arxiv.org/abs/2210.01742v3
• Date: Tue, 27 Jun 2023 13:58:46 GMT
• Title: CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
• Authors: Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, Joao Monteiro
• Abstract summary: This work explores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples. First, we pair self-supervised contrastive learning with the maximum mean discrepancy (MMD) two-sample test. Motivated by this success, we introduce CADet, a novel method for OOD detection of single samples.
• Score: 11.897976063005315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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