SimpleNet: A Simple Network for Image Anomaly Detection and Localization

Abstract

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anoma-lies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features to-wards target domain, (3) a simple Anomaly Feature Gener-ator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three in-tuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, gen-erating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outper-forms previous methods quantitatively and qualitatively. On The MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Further-more, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in per-formance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.