  This paper proposes to use set features for detecting anomalies in samples
that consist of unusual combinations of normal elements. Many leading methods
discover anomalies by detecting an unusual part of a sample. For example,
state-of-the-art segmentation-based approaches, first classify each element of
the sample (e.g., image patch) as normal or anomalous and then classify the
entire sample as anomalous if it contains anomalous elements. However, such
approaches do not extend well to scenarios where the anomalies are expressed by
an unusual combination of normal elements. In this paper, we overcome this
limitation by proposing set features that model each sample by the distribution
of its elements. We compute the anomaly score of each sample using a simple
density estimation method, using fixed features. Our approach outperforms the
previous state-of-the-art in image-level logical anomaly detection and
sequence-level time series anomaly detection.
