# SORTAD: Self-Supervised Optimized Random Transformations for Anomaly Detection in Tabular Data

arXiv: https://arxiv.org/abs/2311.11018 · HTML: https://mlsanity.com/paper/2311.11018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Triplet-Center Loss for Multi-View 3D Object Retrieval](https://mlsanity.com/paper/1803.06189/) (2018)

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

- [Marginal Laplacian Score](https://mlsanity.com/paper/2311.17795/) (2023)

## Abstract (arXiv)

We consider a self-supervised approach to anomaly detection in tabular data. Random transformations are applied to the data, and then each transformation is identified based on its output. These predicted transformations are used to identify anomalies. In tabular data this approach faces many challenges that are related to the uncorrelated nature of the data. These challenges affect the transformations that should be used, as well as the use of their predictions. To this end, we propose SORTAD, a novel algorithm that is tailor-made to solve these challenges. SORTAD optimally chooses random transformations that help the classification process, and have a scoring function that is more sensitive to the changes in the transformations classification prediction encountered in tabular data. SORTAD achieved state-of-the-art results on multiple commonly used anomaly detection data sets, as well as in the overall results across all data sets tested.