SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Journal:
arXiv
Published Date:
Dec 30, 2024
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.
