Towards Total Recall in Industrial Anomaly Detection

Being able to spot defective parts is a critical component in large-scale industrial manufacturing. We further report competitive results on two additional datasets and also find competitive results in the few samples regime.

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• 1 A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only.
• 2 While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically.
• 3 The best peforming approaches combine embeddings from ImageNet models with an outlier detection model.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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