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A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data by Wang, Fengjie; Liu, Chengming; Haibo, Pang; Shi, Lei is a scholarly article available to read on EtoBox.

What is A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data about?

Noise-inclusive fully unsupervised anomaly detection (FUAD) holds significant practical relevance. Although various methods exist to address this problem, they are limited in both performance and scalability. Our work seeks to overcome these obstacles, enabling broader adaptability of unsupervised anomaly detection (UAD) models to FUAD. To achieve this, we introduce the Synergy Scoring Filter (SSFilter), the first fully unsupervised anomaly detection approach to leverage sample-level filtering. SSFilter facilitates end-to-end robust training and applies filtering to the complete training set post-training, offering a model-agnostic solution for FUAD. Specifically, SSFilter integrates a batch-level anomaly scoring mechanism based on mutual patch comparison and utilizes regression errors in anomalous regions, alongside prediction uncertainty, to estimate sample-level uncertainty scores that calibrate the anomaly scoring mechanism. This design produces a synergistic, robust filtering approach. Furthermore, we propose a realistic anomaly synthesis method and an integrity enhancement strategy to improve model training and mitigate missed noisy samples. Our method establishes state-of-th

Author

Wang, Fengjie; Liu, Chengming; Haibo, Pang; Shi, Lei

Published

2025

Language

EN