SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Xi Jiang Ying Chen Qiang Nie Yong Liu Jianlin Liu Bin-Bin Gao Jun Liu Chengjie Wang Feng Zheng
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.