Semi-supervised Anomaly Detection using AutoEncoders
- URL: http://arxiv.org/abs/2001.03674v1
- Date: Mon, 6 Jan 2020 23:06:28 GMT
- Title: Semi-supervised Anomaly Detection using AutoEncoders
- Authors: Manpreet Singh Minhas, John Zelek
- Abstract summary: Anomaly detection refers to the task of finding unusual instances that stand out from the normal data.
In this paper, we present a convolutional auto-encoder architecture for anomaly detection that is trained only on the defect-free (normal) instances.
The approach was tested on two data-sets and achieved an impressive average F1 score of 0.885.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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