mlsanityData to 14 Oct 2026

Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning

arXiv 2209.07005 ↗First posted 15 Sep 2022cs.CVcited by 0

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Abstract (arXiv)

A common study area in anomaly identification is industrial images anomaly detection based on texture background. The interference of texture images and the minuteness of texture anomalies are the main reasons why many existing models fail to detect anomalies. We propose a strategy for anomaly detection that combines dictionary learning and normalizing flow based on the aforementioned questions. The two-stage anomaly detection approach already in use is enhanced by our method. In order to improve baseline method, this research add normalizing flow in representation learning and combines deep learning and dictionary learning. Improved algorithms have exceeded 95% detection accuracy on all MVTec AD texture type data after experimental validation. It shows strong robustness. The baseline method's detection accuracy for the Carpet data was 67.9%. The article was upgraded, raising the detection accuracy to 99.7%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 15 Sep 2022.

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23 of 42 references.

References · 23

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  4. 2019Large-Scale Long-Tailed Recognition in an Open Worldcited by 705
  5. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
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  9. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  10. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  11. 2020Classification-Based Anomaly Detection for General Datacited by 211
  12. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
11 more
  1. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  2. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
  3. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  4. 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 114
  5. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  6. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  7. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  8. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  9. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  10. 2018Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?cited by 17
  11. 2021MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detectioncited by 3

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