DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tack, Jihoon | ko |
dc.contributor.author | Mo, Sangwoo | ko |
dc.contributor.author | Jeong, Jongheon | ko |
dc.contributor.author | Shin, Jinwoo | ko |
dc.date.accessioned | 2020-12-11T06:50:40Z | - |
dc.date.available | 2020-12-11T06:50:40Z | - |
dc.date.created | 2020-12-02 | - |
dc.date.issued | 2020-12-07 | - |
dc.identifier.citation | 34th Conference on Neural Information Processing Systems (NeurIPS) 2020 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278229 | - |
dc.description.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 this https URL. | - |
dc.language | English | - |
dc.publisher | Neural Information Processing Systems | - |
dc.title | CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 34th Conference on Neural Information Processing Systems (NeurIPS) 2020 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Shin, Jinwoo | - |
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