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Overview:
Containing 5354 high-resolution color images of different object and texture categories from several domains and covers various anomalies that differ in attributes such as size, color, and structure.
The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes.
Anomalous regions are highlighted in close-up figures together with their pixel-precise ground truth labels.
Fig. 1 Two objects (hazelnut and metal nut) and one texture (carpet) from the MVTec Anomaly Detection dataset.
Introduction:
Humans are very good at recognizing whether an image is similar to what they have previously observed or whether it is something novel or anomalous. So far, machine learning systems, however, seem to have difficulties with such tasks.
Using unsupervised algorithms to detect anomalous regions.
In the manufacturing industry, for example, optical inspection tasks often lack defective samples that could be used for supervised training or it is unclear which kinds of defects may appear.
A significant amount of interest has been directed towards anomaly detection in natural image data using modern machine learning architectures.
Several terms are used to describe such types of problem settings, such as anomaly detection, novelty detection, outlier detection, or one-class classification.
In this work,
Novelty detection refers to image-level classification settings in which the inlier and outlier distributions differ significantly.
Anomaly detection shall be defined as the task of finding and ideally segmenting anomalies in images that are very close to the training data, i.e., differ only in subtle deviations in possibly very small, confined regions.
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