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Semi-supervised Anomaly Detection using AutoEncoders


Anomaly detection refers to the task of finding unusual instances
that stand out from the normal data. In several applications, these
outliers or anomalous instances are of greater interest compared to
the normal ones. Specifically in the case of industrial optical inspection and infrastructure asset management, finding these defects
(anomalous regions) is of extreme importance. Traditionally and
even today this process has been carried out manually. Humans
rely on the saliency of the defects in comparison to the normal texture to detect the defects. However, manual inspection is slow, tedious, subjective and susceptible to human biases. Therefore, the
automation of defect detection is desirable. But for defect detection
lack of availability of a large number of anomalous instances and
labelled data is a problem. In this paper, we present a convolutional
auto-encoder architecture for anomaly detection that is trained only
on the defect-free (normal) instances. For the test images, residual masks that are obtained by subtracting the original image from
the auto-encoder output are thresholded to obtain the defect segmentation masks. The approach was tested on two data-sets and
achieved an impressive average F1 score of 0.885. The network
learnt to detect the actual shape of the defects even though no defected images were used during the training.