Regional Priority Based Anomaly Detection using Autoencoders

TL;DR

This paper introduces a regional priority autoencoder (RPAE) that improves anomaly detection by focusing on important image regions, addressing the limitations of traditional autoencoders' location invariance.

Contribution

The paper proposes a semi-supervised RPAE model that integrates region proposal networks with category-specific decoders for enhanced anomaly detection.

Findings

RPAE effectively highlights important regions in images for anomaly detection.

The model improves detection accuracy over traditional autoencoders.

Region-based error scoring enhances anomaly localization.

Abstract

In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been proposed. In this model, similar to object detection models, a region proposal network identifies the relevant areas in the images as belonging to one of the predefined categories and then those bounding boxes are fed into appropriate decoder based on the category they belong to. Finally, the error scores from all the decoders are combined based on their importance to provide total reconstruction error.