SimpleNet: A Simple Network for Image Anomaly Detection and Localization
University of Science and Technology of China
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…
Citation impact
- FWCI
- 78.86
- Percentile
- 100%
- References
- 34
Authors
4Topics & keywords
- Discriminator
- Computer science
- Pattern recognition (psychology)
- Anomaly detection
- Artificial intelligence
- Feature (linguistics)
- Feature vector
- Feature extraction
- Reduced inequalities