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We propose a simple and application-friendly network (called SimpleNet) fordetecting and localizing anomalies. SimpleNet consists of four components: (1)a pre-trained Feature Extractor that generates local features, (2) a shallowFeature Adapter that transfers local features towards target domain, (3) asimple Anomaly Feature Generator that counterfeits anomaly features by addingGaussian noise to normal features, and (4) a binary Anomaly Discriminator thatdistinguishes anomaly features from normal features. During inference, theAnomaly Feature Generator would be discarded. Our approach is based on threeintuitions. First, transforming pre-trained features to target-orientedfeatures helps avoid domain bias. Second, generating synthetic anomalies infeature space is more effective, as defects may not have much commonality inthe image space. Third, a simple discriminator is much efficient and practical.In spite of simplicity, SimpleNet outperforms previous methods quantitativelyand qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomalydetection AUROC of 99.6%, reducing the error by 55.5% compared to the next bestperforming model. Furthermore, SimpleNet is faster than existing methods, witha high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNetdemonstrates significant improvements in performance on the One-Class NoveltyDetection task. Code: https://github.com/DonaldRR/SimpleNet.

SimpleNet: A Simple Network for Image Anomaly Detection and Localization