# An optimization method for out-of-distribution anomaly detection models

arXiv: https://arxiv.org/abs/2302.00939 · HTML: https://mlsanity.com/paper/2302.00939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 2 Feb 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features](https://mlsanity.com/paper/2010.07524/) (2020)
- [Uncertainty Aware Proposal Segmentation for Unknown Object Detection](https://mlsanity.com/paper/2111.12866/) (2021)
- [Universal Data Anomaly Detection via Inverse Generative Adversary Network](https://mlsanity.com/paper/2001.08809/) (2020)

## Abstract (arXiv)

Frequent false alarms impede the promotion of unsupervised anomaly detection algorithms in industrial applications. Potential characteristics of false alarms depending on the trained detector are revealed by investigating density probability distributions of prediction scores in the out-of-distribution anomaly detection tasks. An SVM-based classifier is exploited as a post-processing module to identify false alarms from the anomaly map at the object level. Besides, a sample synthesis strategy is devised to incorporate fuzzy prior knowledge on the specific application in the anomaly-free training dataset. Experimental results illustrate that the proposed method comprehensively improves the performances of two segmentation models at both image and pixel levels on two industrial applications.