  Large unlabeled data and difficult-to-identify anomalies are the urgent
issues need to overcome in most industrial scene. In order to address this
issue, a new meth-odology for detecting surface defects in in-dustrial settings
is introduced, referred to as Memory Augmentation and Pseudo-Labeling(MAPL).
The methodology first in-troduces an anomaly simulation strategy, which
significantly improves the model's ability to recognize rare or unknown
anom-aly types by generating simulated anomaly samples. To cope with the
problem of the lack of labeling of anomalous simulated samples, a
pseudo-labeler method based on a one-classifier ensemble was employed in this
study, which enhances the robustness of the model in the case of limited
labeling data by automatically selecting key pseudo-labeling hyperparameters.
Meanwhile, a memory-enhanced learning mechanism is introduced to effectively
predict abnormal regions by analyzing the difference be-tween the input samples
and the normal samples in the memory pool. An end-to-end learning framework is
employed by MAPL to identify the abnormal regions directly from the input data,
which optimizes the ef-ficiency and real-time performance of de-tection. By
conducting extensive trials on the recently developed BHAD dataset (in-cluding
MVTec AD [1], Visa [2], and MDPP [3]), MAPL achieves an average im-age-level
AUROC score of 86.2%, demon-strating a 5.1% enhancement compared to the
original MemSeg [4] model. The source code is available at
https://github.com/jzc777/MAPL.
