# Boosting Anomaly Detection Using Unsupervised Diverse Test-Time Augmentation

arXiv: https://arxiv.org/abs/2110.15700 · HTML: https://mlsanity.com/paper/2110.15700/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Extending Isolation Forest for Anomaly Detection in Big Data via K-Means](https://mlsanity.com/paper/2104.13190/) (2021)
- [An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies](https://mlsanity.com/paper/2101.03141/) (2020)

## Built on by (most cited first, 1 of 1)

- [BiasGuard: Guardrailing Fairness in Machine Learning Production Systems](https://mlsanity.com/paper/2501.04142/) (2025)

## Abstract (arXiv)

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.