Generation of Normal Model and Anomaly Detection by Adversarial AutoEncoder under Small Number of Defective Samples

Abstract

For industrial products and foods, it is essential to conduct a visual inspection to improve the quality of products. In recent years, automation by a neural network has been considered but learning a neural networks requires a lot of good and defective samples. However it is so difficult to ensure a lot of defective samples that neural networks cannot learn properly. In this paper, we aimed at discrimination of defects under conditions where there is a large number of good products and a small number of defective products. By combining AAE, which can extract features following any distribution and Hotelling's T-Square, which is an effective anomaly detection method when data follows a normal distribution, it is possible to discriminate defects under a small number of defective samples. We experimented on 2 dataset and showed the effectiveness.