# Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

arXiv: https://arxiv.org/abs/2203.14539 · HTML: https://mlsanity.com/paper/2203.14539/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [MNIST-C: A Robustness Benchmark for Computer Vision](https://mlsanity.com/paper/1906.02337/) (2019)
- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [Rethinking Assumptions in Deep Anomaly Detection](https://mlsanity.com/paper/2006.00339/) (2020)
- [A Meta-Analysis of the Anomaly Detection Problem](https://mlsanity.com/paper/1503.01158/) (2015)

## Built on by (most cited first, 2 of 2)

- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Anomaly Detection via Autoencoder Composite Features and NCE](https://mlsanity.com/paper/2502.01920/) (2025)

## Abstract (arXiv)

The unlabeled data are generally assumed to be normal data in detecting abnormal data via semisupervised learning. This assumption, however, causes inevitable detection error when distribution of unlabeled data is different from distribution of labeled normal dataset. To deal the problem caused by distribution gap between labeled and unlabeled data, we propose a semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The proposed SAD-KL is composed of two steps: (1) estimating KL divergence of probability density functions (PDFs) of the local outlier factors (LOFs) of the labeled normal data and the unlabeled data (2) estimating detection probability and threshold for detecting normal data in unlabeled data by using the KL divergence. We show that the PDFs of the LOFs follow Burr distribution and use them for detection. Once the threshold is computed, the SAD-KL runs iteratively until the labeling change rate is lower than the predefined threshold. Experiments results show that the SAD-KL shows superior detection probability over the existing algorithms even though it takes less learning time.