Deep Feature Inpainting for Unsupervised Visual Anomaly Detection

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. In this paper, we address the problem of unsupervised visual anomaly detection from the perspective of image feature restoration. We propose deep feature inpainting (DFI) where we develop a deep learning model that can automatically inpainting the normal image patterns or features from the anomalous images by eliminating the irregular structures or patterns. By comparing the anomalous features and their reconstructed counterparts, we can identify and analyze the anomalous structures in the images. Extensive experiments have been carried out to demonstrate the effectiveness of the approach.