Anomaly Detection with SoftPatch: Enhancing Industrial Applications
Introduction
Anomaly detection (AD) is essential in industrial applications to identify unexpected events in input data. It is particularly challenging due to the complexity of defects, which can be tiny and hard to collect.
Challenges and Solutions
Unsupervised AD methods often struggle with noisy data in real-world settings, affecting model performance. SoftPatch, a novel algorithm, addresses this challenge by utilizing the outlier factor to achieve better noise robustness.
Key Features of SoftPatch
SoftPatch filters noisy data using a noise discriminator before the coreset construction process, effectively softening the search process. It distinguishes noise at the patch level and groups features by position to determine noise patches and remove noisy features. The algorithm then calculates anomaly scores, considering the local relationship around the nearest node, increasing its robustness.
Performance and Results
Evaluation of SoftPatch in various noise scenes demonstrates its outperformance of state-of-the-art AD methods on benchmark datasets, highlighting its effectiveness.
Conclusion and Future Prospects
This research emphasizes the importance of investigating noisy data in unsupervised AD and provides a new view for further research. SoftPatch has the potential to improve the efficiency and performance of industrial inspection systems.
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