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Contrastive Learning for Novelty Detection
Jinwoo Shin

Fri Jul 23 01:30 PM -- 02:00 PM (PDT) @
in Uncertainty and Robustness in Deep Learning »
Event URL: https://alinlab.kaist.ac.kr/resource/csi_v0.pdf »

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 talk, I will present 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. This is a joint work with Jihoon Tack, Sangwoo Mo and Jongheon Jeong (all from KAIST).

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Jinwoo Shin (KAIST)

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