RISING STAR
Young pioneer recognised for anomaly detection advance
We speak to Karsten Roth about PatchCore, anomaly detection AI technology that outperforms competitor methods
K
arsten Roth, a PhD researcher with the Explainable
Machine Learning group at the University of Tübingen, has won the EMVA Young Professional Award for work on a neural network for anomaly detection. Roth was presented with
the award at the 2022 EMVA business conference in Brussels. PatchCore, which Roth
developed during a research internship at Amazon AWS, is an automated visual anomaly detection method addressing the cold-start problem – that is, the model only has access to non-defective example images during training. Te model can determine defects without having seen them. Te model offers competitive
inference times while outperforming competitor methods for both detection and localisation. On the challenging, widely used MVTec anomaly detection benchmark, PatchCore achieves an image- level anomaly detection Auroc score – a metric for classifying a model’s performance – of 99.6 per cent, more than halving the error compared to the next best competitor. It’s scalable and a lot more sample efficient too, according
to Roth, and can match the previous state-of-the-art methods with as little as three per cent of the training data. Roth presented the work
at the Computer Vision and Pattern Recognition (CVPR) conference in June 2022.
Memory bank subsampling One of the keys to PatchCore’s performance is the subsampling method Roth used – coreset subsampling rather than random subsampling – to trim the memory bank. Coreset subsampling, unlike random subsampling, aims to retain overall coverage of the feature space in the memory bank. PatchCore’s network
will generate a feature representation for different locations in an image, which it then dumps in a memory bank. Te danger is the memory bank gets very large very quickly, and so subsampling is used to keep its size manageable. Te problem with random
subsampling is there’s the potential to drop rarely occurring feature sets. Tis is not the case for coreset subsampling. Speaking to Imaging and Machine Vision Europe, Roth explained: ‘[Using coreset subsampling] we are able to reduce the memory bank
28 IMAGING AND MACHINE VISION EUROPE VISION YEARBOOK 2022/23
Karsten Roth (left) receiving the award from Chris Yates, EMVA president
significantly with minimal drop in performance. Tis makes approaches that operate on this memory bank significantly – by orders of magnitude – quicker than ones that operate on the big memory bank, but without a drop in performance.’ Test images are then
compared to the feature sets in the trimmed memory bank. If the feature is significantly different from the ones in the memory bank, it’s likely to be a defect. ‘Te result is a method that
has only ever seen normal data but, when you apply it to test data, it is able to very accurately detect defects for all kinds of data and products,’ Roth said. Te MVTec anomaly
‘It’s very nice to receive this award… because it was research that was built around practical needs’
detection benchmark gives 15 different anomaly detection tasks, with the final performance as an average across all the tasks. For each of the 15 anomaly detection tasks, PatchCore achieves above 90 per cent Auroc with just five images of normal data; 15 images gives more than 95 per
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EMVA
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