  Anomaly detection is a well-known task that involves the identification of
abnormal events that occur relatively infrequently. Methods for improving
anomaly detection performance have been widely studied. However, no studies
utilizing test-time augmentation (TTA) for anomaly detection in tabular data
have been performed. TTA involves aggregating the predictions of several
synthetic versions of a given test sample; TTA produces different points of
view for a specific test instance and might decrease its prediction bias. We
propose the Test-Time Augmentation for anomaly Detection (TTAD) technique, a
TTA-based method aimed at improving anomaly detection performance. TTAD
augments a test instance based on its nearest neighbors; various methods,
including the k-Means centroid and SMOTE methods, are used to produce the
augmentations. Our technique utilizes a Siamese network to learn an advanced
distance metric when retrieving a test instance's neighbors. Our experiments
show that the anomaly detector that uses our TTA technique achieved
significantly higher AUC results on all datasets evaluated.
