SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation

TL;DR

SoftPatch+ introduces a fully unsupervised anomaly detection method that effectively denoises data at the patch level, outperforming existing methods especially in noisy industrial scenarios.

Contribution

This paper is the first to address fully unsupervised industrial anomaly detection with noisy data, proposing SoftPatch and SoftPatch+ for robust, patch-level denoising and anomaly classification.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Maintains high performance in high-noise scenarios (10%-40%).

Comparable to noise-free methods in standard settings.

Abstract

Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper is the first to consider fully unsupervised industrial anomaly detection (i.e., unsupervised AD with noisy data). To solve this problem, we proposed memory-based unsupervised AD methods, SoftPatch and SoftPatch+, which efficiently denoise the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset, and SoftPatch+ has more robust performance which is articularly useful in real-world industrial inspection scenarios with high levels of noise (from 10% to 40%). Comprehensive experiments conducted in diverse noise scenarios demonstrate that both SoftPatch and SoftPatch+ outperform the state-of-the-art AD methods on the MVTecAD, ViSA, and BTAD benchmarks. Furthermore, the performance of SoftPatch and SoftPatch+ is comparable to that of the noise-free methods in conventional unsupervised AD setting. The code of the proposed methods can be found at https://github.com/TencentYoutuResearch/AnomalyDetection-SoftPatch.