mlsanityData to 14 Oct 2026

Exploring the Relationship between Samples and Masks for Robust Defect Localization

arXiv 2306.10720 ↗First posted 19 Jun 2023cs.CVcited by 0

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Abstract (arXiv)

Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability.This paper proposes a one-stage framework that detects defective patterns directly without the modeling process.This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping.Experimental results on the texture class of the challenging MVTec AD dataset show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 19 Jun 2023.

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24 of 30 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  6. 2013Describing Textures in the Wildcited by 2,125
  7. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  8. 2017Random Erasing Data Augmentationcited by 1,239
  9. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  10. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  11. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  12. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
12 more
  1. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  4. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  5. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  6. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  7. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  8. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  9. 2019U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translationcited by 186
  10. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  11. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  12. 2023PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flowcited by 74

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