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image-level anomaly score

An image-level anomaly score is a single quantitative value assigned to an entire image that measures its degree of abnormality or deviation from expected normal patterns. In computer vision and visual inspection, this scalar metric reflects the overall likelihood that an image contains defects, irregularities, or outliers, distinguishing it from pixel-level or patch-level scores that localize specific anomalous regions within the visual field. Algorithms typically compute this score by evaluating global feature representations against a baseline model of normal data or by aggregating localized patch-level anomaly scores, often using the maximum or top-ranked local anomaly value. The resulting score serves as a global decision criterion, enabling automated systems to classify an entire image as normal or anomalous through thresholding.

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SoftPatch: Unsupervised Anomaly Detection with Noisy Data

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, Feng Zheng

OrganizationsDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyTencent

Why you should read this

Proposes SoftPatch, a patch-level denoising and memory re-weighting method that prevents defective training samples from distorting decision boundaries in real-world unsupervised anomaly detection.

Although mainstream unsupervised anomaly detection (AD) 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 considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises 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. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

Added

2026-09-26