mlsanityData to 7 Oct 2026

A Prototype-Based Neural Network for Image Anomaly Detection and Localization

arXiv 2310.02576 ↗First posted 4 Oct 2023cs.CVcited by 7

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Abstract (arXiv)

Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with L2 feature normalization, a 1×1 convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the 1×1 convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on two challenging industrial anomaly detection datasets, MVTec AD and BTAD, demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The source code is available at: https://github.com/98chao/ProtoAD.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

27 of 44 references.

References · 27

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15 more
  1. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  2. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  3. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptationcited by 6
  2. 2025Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixturecited by 5
  3. 2024Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detectioncited by 1
  4. 2026Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutionscited by 1
  5. 2024Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localizationcited by 0
  6. 2025Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localizationcited by 0
  7. 2026Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detectioncited by 0