  Fine-grained anomaly detection has recently been dominated by segmentation
based approaches. These 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 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 its elements. We compute
the anomaly score of each sample using a simple density estimation method. Our
simple-to-implement approach outperforms the state-of-the-art in image-level
logical anomaly detection (+3.4%) and sequence-level time-series anomaly
detection (+2.4%).
