Published in Neural Information Processing Systems (NeurIPS), 2020

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tl;dr: Contrastive representations are surprisingly good at discriminating OOD samples, and contrasting also “OOD-like” augmentations can further improve their performances.

  • Won Qualcomm Innovation Fellowship Korea 2020
Additional information


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


 author = {Tack, Jihoon and Mo, Sangwoo and Jeong, Jongheon and Shin, Jinwoo},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {11839--11852},
 publisher = {Curran Associates, Inc.},
 title = {CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances},
 url = {},
 volume = {33},
 year = {2020}