FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

TL;DR

FUN-AD introduces a fully unsupervised anomaly detection method that effectively handles noisy training data by leveraging pairwise feature distances and a novel loss function, validated on industrial benchmarks.

Contribution

The paper presents a new unsupervised anomaly detection approach using an iteratively reconstructed memory bank and a pairwise similarity loss to improve robustness against noisy data.

Findings

Effective on industrial anomaly benchmarks

Robust to noisy and contaminated training data

Outperforms existing unsupervised methods

Abstract

While the mainstream research in anomaly detection has mainly followed the one-class classification, practical industrial environments often incur noisy training data due to annotation errors or lack of labels for new or refurbished products. To address these issues, we propose a novel learning-based approach for fully unsupervised anomaly detection with unlabeled and potentially contaminated training data. Our method is motivated by two observations, that i) the pairwise feature distances between the normal samples are on average likely to be smaller than those between the anomaly samples or heterogeneous samples and ii) pairs of features mutually closest to each other are likely to be homogeneous pairs, which hold if the normal data has smaller variance than the anomaly data. Building on the first observation that nearest-neighbor distances can distinguish between confident normal samples and anomalies, we propose a pseudo-labeling strategy using an iteratively reconstructed memory bank (IRMB). The second observation is utilized as a new loss function to promote class-homogeneity between mutually closest pairs thereby reducing the ill-posedness of the task. Experimental results on two public industrial anomaly benchmarks and semantic anomaly examples validate the effectiveness of FUN-AD across different scenarios and anomaly-to-normal ratios. Our code is available at https://github.com/HY-Vision-Lab/FUNAD.