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3VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, and G. Foresti. 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), page 01-06. (2021)
30 days ago by @andolab
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Anomaly_detection
Dataset
Anomaly_detectionDataset
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3SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation
Y. Zou, J. Jeong, L. Pemula, D. Zhang, and O. Dabeer. Computer Vision -- ECCV 2022, page 392--408. Cham, Springer Nature Switzerland, (2022)
30 days ago by @andolab
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Anomaly_Detection
Dataset
Anomaly_DetectionDataset
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2Image-level Synthesis and Perturbation for Self-supervised Anomaly Detection
R. Kosugi, and S. Ando. 2025 6th International Conference on Machine Learning and Human-Computer Interaction (MLHMI), page 6-10. (2025)
30 days ago by @andolab
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Anomaly_Detection
Anomaly_Synthesis
Anomaly_DetectionAnomaly_Synthesis
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4Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
M. Rudolph, B. Wandt, and B. Rosenhahn. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), page 1906-1915. (2021)
a month ago by @andolab
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Anomaly_Detection
Normalizing-flow
Anomaly_DetectionNormalizing-flow
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2Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
X. Yao, R. Li, Z. Qian, L. Wang, and C. Zhang. Computer Vision -- ECCV 2024, page 92--108. Cham, Springer Nature Switzerland, (2025)
a month ago by @andolab
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Anomaly_Detection
Normalizing-flow_model
Anomaly_DetectionNormalizing-flow_model
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2PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
T. Defard, A. Setkov, A. Loesch, and R. Audigier. Pattern Recognition. ICPR International Workshops and Challenges, page 475--489. Cham, Springer International Publishing, (2021)
a month ago by @andolab
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Anomaly_Detection
Few-shot_Learning
Anomaly_DetectionFew-shot_Learning
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2SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
J. Yoon, K. Sohn, C. Li, S. Arik, and T. Pfister. Transactions on Machine Learning Research, (2023)Featured Certification.
a month ago by @andolab
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Anomaly_Detection
Distribution_Mismatch
Semi-supervised_learning
Anomaly_DetectionDistribution_MismatchSemi-supervised_learning
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3AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Z. Gu, B. Zhu, G. Zhu, Y. Chen, M. Tang, and J. Wang. Proceedings of the AAAI Conference on Artificial Intelligence, 38 (3): 1932-1940 (March 2024)
a month ago by @andolab
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Anomaly_Detection
Vision-Language_Model
Anomaly_DetectionVision-Language_Model
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3WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
J. Jeong, Y. Zou, T. Kim, D. Zhang, A. Ravichandran, and O. Dabeer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page 19606-19616. (June 2023)
a month ago by @andolab
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Anomaly_Detection
Vision-Language_Model
Anomaly_DetectionVision-Language_Model
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3ResAD: A Simple Framework for Class Generalizable Anomaly Detection
X. Yao, Z. Chen, C. Gao, G. Zhai, and C. Zhang. Advances in Neural Information Processing Systems, 37, page 125287--125311. Curran Associates, Inc., (2024)
a month ago by @andolab
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Anomaly_Detection
Few-shot_Learning
Anomaly_DetectionFew-shot_Learning
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3Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
X. Yao, R. Li, J. Zhang, J. Sun, and C. Zhang. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page 24490-24499. (2023)
a month ago by @andolab
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Anomaly_Detection
Supervised-learning
Anomaly_DetectionSupervised-learning
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3CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization
S. Lee, S. Lee, and B. Song. IEEE Access, (2022)
a year ago by @andolab
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Anomaly_Detection
Feature-Adaptation
Hypersphere
Anomaly_DetectionFeature-AdaptationHypersphere
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2Anomaly Detection via Reverse Distillation from One-Class Embedding
H. Deng, and X. Li. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page 9727-9736. (2022)
a year ago by @andolab
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Anomaly_Detection
One-class
Anomaly_DetectionOne-class
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1Explainable Deep One-Class Classification
P. Liznerski, L. Ruff, R. Vandermeulen, B. Franks, M. Kloft, and K. Muller. International Conference on Learning Representations, (2021)
a year ago by @andolab
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Anomaly_detection
Explainability
Anomaly_detectionExplainability
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1Describing Textures in the Wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi. (2013)
a year ago by @andolab
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Anomaly_Detection
image
Anomaly_Detectionimage
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1Deep Nearest Neighbor Anomaly Detection.
L. Bergman, N. Cohen, and Y. Hoshen. CoRR, (2020)
2 years ago by @andolab
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Anomaly_Detection
Deep_Nearest_Neighbor
Anomaly_DetectionDeep_Nearest_Neighbor
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1Image Anomaly Detection with Generative Adversarial Networks
L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, and M. Kloft. Machine Learning and Knowledge Discovery in Databases, page 3--17. Cham, Springer International Publishing, (2019)
3 years ago by @andolab
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Anomaly_detection
GAN
Anomaly_detectionGAN
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3Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
B. Zong, Q. Song, M. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen. International Conference on Learning Representations, (2018)
3 years ago by @andolab
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Anomaly_detection
Autoencoder
Anomaly_detectionAutoencoder
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3Deep Semi-Supervised Anomaly Detection
L. Ruff, R. Vandermeulen, N. Görnitz, A. Binder, E. Müller, K. Müller, and M. Kloft. International Conference on Learning Representations, (2020)
4 years ago by @andolab
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Anomaly_Detection
Deep_Learning
Semi-Supervised_Learning
Anomaly_DetectionDeep_LearningSemi-Supervised_Learning
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2Explainable Deep Few-shot Anomaly Detection with Deviation Networks
G. Pang, C. Ding, C. Shen, and A. van den Hengel. (2021)
4 years ago by @andolab
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Anomaly_Detection
Few-shot_Learning
Anomaly_DetectionFew-shot_Learning
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3VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

3SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation

2Image-level Synthesis and Perturbation for Self-supervised Anomaly Detection

4Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

2Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection

2PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization

2SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch

3AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

3WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

3ResAD: A Simple Framework for Class Generalizable Anomaly Detection

3Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

3CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization

2Anomaly Detection via Reverse Distillation from One-Class Embedding

1Explainable Deep One-Class Classification

1Describing Textures in the Wild

1Deep Nearest Neighbor Anomaly Detection.

1Image Anomaly Detection with Generative Adversarial Networks

3Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

3Deep Semi-Supervised Anomaly Detection

2Explainable Deep Few-shot Anomaly Detection with Deviation Networks

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