Image Anomaly Detection by Aggregating Deep Pyramidal Representations
This addresses anomaly detection for applications like industrial defect detection and medical imaging, but it is incremental as it builds on standard autoencoder methods.
The paper tackled image anomaly detection by using a deep neural network with pyramid levels to analyze features at different scales, achieving good accuracy on MNIST, FMNIST, and MVTec datasets.
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