Image Anomaly Detection by Aggregating Deep Pyramidal Representations

TL;DR

This paper introduces a deep pyramid-based autoencoder approach for image anomaly detection, leveraging multi-scale features to identify deviations from normal data with high accuracy.

Contribution

It proposes a novel encoding-decoding network architecture that aggregates deep pyramidal representations for improved anomaly detection.

Findings

High accuracy on MNIST, FMNIST, and MVTec datasets

Effective detection of anomalies via reconstruction failure

Utilizes normal data only for training

Abstract

Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset