A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data

TL;DR

This paper introduces SSFilter, a novel unsupervised anomaly detection method that uses sample-level filtering based on mutual patch comparison and uncertainty estimation, improving robustness and scalability in noisy data scenarios.

Contribution

We propose SSFilter, the first fully unsupervised anomaly detection approach leveraging sample-level filtering, enhancing performance and scalability in noisy, real-world datasets.

Findings

Achieves state-of-the-art results on the Real-IAD dataset.

Dataset-level filtering improves various UAD methods.

High scalability enhances practical applicability.

Abstract

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-the-art performance on the FUAD benchmark of the recent large-scale industrial anomaly detection dataset, Real-IAD. Additionally, dataset-level filtering enhances the performance of various UAD methods on the FUAD benchmark, and the high scalability of our approach significantly boosts its practical applicability.