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[2106.08265v2] Towards Total Recall in Industrial Anomaly Detection
Source: arxiv.org
Article summary:
1. This paper proposes PatchCore, a maximally representative memory bank of nominal patch-features, to address the cold-start problem in industrial anomaly detection.
2. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization.
3. On the challenging MVTec AD benchmark, PatchCore achieves an image-level anomaly detection AUROC score of up to 99.6%.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for its claims in the form of results from experiments conducted on three datasets. The authors also provide code for their proposed method, which further adds to the credibility of their work. However, there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative approaches to solving the cold-start problem in industrial anomaly detection. Additionally, they do not discuss any possible risks associated with using their proposed method or any potential limitations that may arise from its use. Furthermore, they do not present both sides equally when discussing their results; instead they focus mainly on how well their approach performs compared to other methods without providing much detail about those methods or why they may be less effective than PatchCore. Finally, there is a lack of discussion about how this work could be applied in practice or what implications it may have for industry applications.