  Anomaly detection aims at identifying data points that show systematic
deviations from the majority 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 labels to each datum
(normal vs. anomalous) while updating the model parameters. Inspired by outlier
exposure (Hendrycks et al., 2018) that considers 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 experiments 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.
