CVPR 2023 – SimpleNet: A Simple Network for Image Anomaly Detection and Localization

In this episode we discuss SimpleNet: A Simple Network for Image Anomaly Detection and Localization
by Zhikang Liu, Yiming Zhou, Yuansheng Xu, Zilei Wang. The paper introduces a new deep learning network called SimpleNet for detecting and localizing anomalies. SimpleNet has four main components that include a pre-trained Feature Extractor, a shallow Feature Adapter, a simple Anomaly Feature Generator, and a binary Anomaly Discriminator. The authors base their approach on three intuitions which involve transforming pre-trained features to target-oriented features, generating synthetic anomalies in feature space, and using a simple discriminator. SimpleNet performs better than previous methods on the MVTec AD benchmark with an anomaly detection AUROC of 99.6% and a high frame rate of 77 FPS on a 3080ti GPU.