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Title: Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Anomaly detection aims at identifying data points that show systematic deviations from the major- ity of data in an unlabeled dataset. A common assumption is that clean training data (free of anomalies) is available, which is often violated in practice. We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models. The idea is to jointly infer binary la- bels to each datum (normal vs. anomalous) while updating the model parameters. Inspired by out- lier exposure (Hendrycks et al., 2018) that con- siders synthetically created, labeled anomalies, we thereby use a combination of two losses that share parameters: one for the normal and one for the anomalous data. We then iteratively proceed with block coordinate updates on the parameters and the most likely (latent) labels. Our exper- iments with several backbone models on three image datasets, 30 tabular data sets, and a video anomaly detection benchmark showed consistent and significant improvements over the baselines.  more » « less
Award ID(s):
2047418 2007719 2003237
PAR ID:
10347068
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
162
ISSN:
2640-3498
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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