SimpleNet: A Simple Network for Image Anomaly Detection and Localization

By Zhikang Liu, Yiming Zhou, Yuansheng Xu, Zilei Wang. Computer Vision and Pattern Recognition, 2023

600 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a23c0a89bd21bd2481fedcdd6d1ac891c6c06bdc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kilian Batzner
2. Lars Heckler
3. Rebecca König

In Computer Vision and Pattern Recognition

1. Kilian Batzner
2. Lars Heckler
3. Rebecca König

In Radiology, Nuclear Medicine and Imaging

1. Kilian Batzner
2. Lars Heckler
3. Rebecca König

Citations in the corpus

• EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies (2023, methodology): “For SimpleNet, we are able to reproduce the official re-sults but find that SimpleNet tunes the training duration on the test images of a scenario.”