We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator 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 intuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, generating 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 outperforms 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. Furthermore, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in performance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.

翻译：我们提出了一种简单且易于应用的网络（称为SimpleNet），用于检测和定位异常。SimpleNet由四个组件组成：(1) 预训练的特征提取器，用于生成局部特征；(2) 浅层特征适配器，将局部特征迁移至目标领域；(3) 简单的异常特征生成器，通过向正常特征添加高斯噪声来伪造异常特征；(4) 二元异常判别器，用于区分异常特征与正常特征。在推理阶段，异常特征生成器将被丢弃。我们的方法基于三个直觉：首先，将预训练特征转换为面向目标的特征有助于避免领域偏差；其次，在特征空间中生成合成异常更有效，因为缺陷在图像空间中可能缺乏共现性；第三，简单的判别器更加高效和实用。尽管结构简单，SimpleNet在定量和定性上均优于以往方法。在MVTec AD基准测试中，SimpleNet实现了99.6%的异常检测AUROC，相比次优模型降低了55.5%的误差。此外，SimpleNet比现有方法更快，在3080ti GPU上实现了77 FPS的高帧率。同时，SimpleNet在单类新奇检测任务上展现出显著的性能提升。代码链接：https://github.com/DonaldRR/SimpleNet。