![]() The body of the CNN is now ready to output features that can be used to calculate the distance in between data.The modified CNN can be trained in classification tasks as usual, even without any change on the training process, then it’s done.They are very simple just to reuse conventional CNN models with small additional plug-in layers. ![]() ![]() The distance is then used to determine if the faces in two photos have the same identities or not, for example.Ī big benefit of these deep metric learning methods is their simpleness. Major deep metric learning such as ArcFace/ CosFace are popular in face verification/recognition tasks, and these methods can measure the distance between data. This article made some experiments to apply deep metric learning to solve anomaly detection tasks with this dataset. This dataset comes with a paper which not only introduces the dataset but also evaluates baseline methods such as GAN, autoencoder, or other traditional methods. MVTec AD is introduced to play the role of MNIST, CIFAR10, or ImageNet for unsupervised anomaly detection (and segmentation) research area. ![]() “To the best of our knowledge, no comparable dataset exists for the task of unsupervised anomaly detection.” Figure from MVTec AD website: Good (green) and Bad (red) examples from 6 categories.Ĭategories from industrial to agricultural, defects from each different domain, with various alignments in the images, and even with segmentation data of defect areas in the annotations - it’s a great dataset.
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