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Anomaly detection aims at identifying data points that show systematicdeviations from the majority of data in an unlabeled dataset. A commonassumption is that clean training data (free of anomalies) is available, whichis often violated in practice. We propose a strategy for training an anomalydetector in the presence of unlabeled anomalies that is compatible with a broadclass of models. The idea is to jointly infer binary labels to each datum(normal vs. anomalous) while updating the model parameters. Inspired by outlierexposure (Hendrycks et al., 2018) that considers synthetically created, labeledanomalies, we thereby use a combination of two losses that share parameters:one for the normal and one for the anomalous data. We then iteratively proceedwith block coordinate updates on the parameters and the most likely (latent)labels. Our experiments with several backbone models on three image datasets,30 tabular data sets, and a video anomaly detection benchmark showed consistentand significant improvements over the baselines.

Latent Outlier Exposure for Anomaly Detection with Contaminated Data