Boosting anomaly detection using unsupervised diverse test-time augmentation

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 improvements of 0.037 AUC (3.7%) using Autoencoder, 0.016 AUC (1.6%) using OC-SVM, and 0.023 AUC (2.3%) using LOF.