Transcription of Patch SVDD: Patch-level SVDD for Anomaly Detection and ...
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Patch SVDD: Patch-level SVDD for Anomaly Detection and ...
Patch SVDD: Patch -level SVDDfor Anomaly Detection and SegmentationJihun Yi[0000 0001 5762 6643]and Sungroh Yoon [0000 0002 2367 197X]Data Science and Artificial Intelligence LaboratoryElectrical and Computer EngineeringSeoul National University, Seoul, South this paper, we address the problem of image anomalydetection and segmentation. Anomaly Detection involves making a binarydecision as to whether an input image contains an Anomaly , and anomalysegmentation aims to locate the Anomaly on the pixel level. Support vectordata description (SVDD) is a long-standing algorithm used for ananomalydetection, and we extend its deep learning variant to the Patch -basedmethod using self-supervised learning.
patch, not the entire image, as illustrated in Fig. 2. Accordingly, inspection is performed for each patch. Patch-wise inspection has several advantages. First, the inspection result is available at each position, and hence we can localize the positions of defects. Second, such fine-grained examination improves overall detection performance.
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