Anomaly localization by modeling perceptual features
- URL: http://arxiv.org/abs/2008.05369v1
- Date: Wed, 12 Aug 2020 15:09:13 GMT
- Title: Anomaly localization by modeling perceptual features
- Authors: David Dehaene, Pierre Eline
- Abstract summary: Feature-Augmented VAE is trained by reconstructing the input image in pixel space, and also in several different feature spaces.
It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.
- Score: 3.04585143845864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although unsupervised generative modeling of an image dataset using a
Variational AutoEncoder (VAE) has been used to detect anomalous images, or
anomalous regions in images, recent works have shown that this method often
identifies images or regions that do not concur with human perception, even
questioning the usability of generative models for robust anomaly detection.
Here, we argue that those issues can emerge from having a simplistic model of
the anomaly distribution and we propose a new VAE-based model expressing a more
complex anomaly model that is also closer to human perception. This
Feature-Augmented VAE is trained by not only reconstructing the input image in
pixel space, but also in several different feature spaces, which are computed
by a convolutional neural network trained beforehand on a large image dataset.
It achieves clear improvement over state-of-the-art methods on the MVTec
anomaly detection and localization datasets.
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