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Boosting Anomaly Detection Using Unsupervised Diverse Test-time Augmentation by Seffi Cohen; Niv Goldshlager; Lior Rokach; Bracha Shapira is a Computer Science article available to read on EtoBox.

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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 using our TTA technique significantly improves the performance of anomaly detection algorithms, as evidenced by the higher AUC results achieved on all datasets evaluated. Specifically, we observed average improv

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Author

Seffi Cohen; Niv Goldshlager; Lior Rokach; Bracha Shapira

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)