CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

• URL: http://arxiv.org/abs/2007.08176v2
• Date: Wed, 21 Oct 2020 08:09:43 GMT
• Title: CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
• Authors: Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin
• Abstract summary: We propose a simple, yet effective method named contrasting shifted instances (CSI) In addition to contrasting a given sample with other instances as in conventional contrastive learning methods, our training scheme contrasts the sample with distributionally-shifted augmentations of itself. Our experiments demonstrate the superiority of our method under various novelty detection scenarios.
• Score: 77.28192419848901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation. In this paper, we propose a simple, yet effective method named contrasting shifted instances (CSI), inspired by the recent success on contrastive learning of visual representations. Specifically, in addition to contrasting a given sample with other instances as in conventional contrastive learning methods, our training scheme contrasts the sample with distributionally-shifted augmentations of itself. Based on this, we propose a new detection score that is specific to the proposed training scheme. Our experiments demonstrate the superiority of our method under various novelty detection scenarios, including unlabeled one-class, unlabeled multi-class and labeled multi-class settings, with various image benchmark datasets. Code and pre-trained models are available at https://github.com/alinlab/CSI.

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