Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization | Paper Citation Record

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This paper cites International Journal of Computer Vision130(4), 947–969 (2022).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization International Journal of Computer Vision130(4), 947–969 (2022)

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This paper cites In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 2

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This paper cites International Conference on Learning Repre- sentations (2019).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization International Conference on Learning Repre- sentations (2019)

Reference 3

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This paper cites Advances in neural information processing systems 35, 39090–39102 (2022) Prototype-based anomaly detection with optimal transport 15.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in neural information processing systems 35, 39090–39102 (2022) Prototype-based anomaly detection with optimal transport 15

Reference 4

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This paper cites Advances in neural information processing systems33, 9912–9924 (2020).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in neural information processing systems33, 9912–9924 (2020)

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This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Sub-Image Anomaly Detection with Deep Pyramid Correspondences

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Unresolved cited work

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Advances in Neural Information Processing Systems

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This paper cites In: Proceedings of the European conference on computer vision (ECCV).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the European conference on computer vision (ECCV)

Reference 9

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 10

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This paper cites In: Proceedings of the IEEE/CVF interna- tional conference on computer vision.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF interna- tional conference on computer vision

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This paper cites Communications of the ACM 63(11), 139–144 (2020).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Communications of the ACM 63(11), 139–144 (2020)

Reference 12

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Pro- ceedings of the IEEE/CVF International Conference on Computer Vision

Reference 13

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Unresolved cited work

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This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in neural information processing systems33, 6840–6851 (2020)

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the ieee/cvf conference on computer vision and pattern recognition

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 17

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This paper cites ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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This paper cites Learning with Mixture of Prototypes for Out-of-Distribution Detection.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Learning with Mixture of Prototypes for Out-of-Distribution Detection

Reference 20

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This paper cites Advances in Neural Information Processing Systems36, 17602–17622 (2023) 16 R.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in Neural Information Processing Systems36, 17602–17622 (2023) 16 R

Reference 21

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This paper cites ArXiv e-prints (2018).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization ArXiv e-prints (2018)

Reference 22

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 23

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: International conference on machine learning

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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This paper cites In: Proceedings of the 35th International Conference on Machine Learning.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the 35th International Conference on Machine Learning

Reference 26

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Unresolved cited work

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This paper cites In: European Conference on Com- puter Vision.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: European Conference on Com- puter Vision

Reference 28

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This paper cites IEEE Transactions on Industrial Informatics 19(7), 8072–8082 (2023).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization IEEE Transactions on Industrial Informatics 19(7), 8072–8082 (2023)

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Ad- vances in neural information processing systems30 (2017)

Reference 30

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This paper cites Machine learning 54, 45–66 (2004).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Machine learning 54, 45–66 (2004)

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This paper cites Advances in neural information processing systems35, 21792–21804 (2022).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in neural information processing systems35, 21792–21804 (2022)

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This paper cites FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

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This paper cites Advances in neural information processing systems30 (2017).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in neural information processing systems30 (2017)

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the 36th International Conference on Machine Learning

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This paper cites Advances in Neural Infor- mation Processing Systems35, 11800–11814 (2022).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Advances in Neural Infor- mation Processing Systems35, 11800–11814 (2022)

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This paper cites Student-Teacher Feature Pyramid Matching for Anomaly Detection.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Student-Teacher Feature Pyramid Matching for Anomaly Detection

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization IEEE Transac- tions on Cybernetics54(5), 2720–2733 (2024)

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF international conference on computer vision

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the AAAI conference on artificial intelligence

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This paper cites In: European Conference on Computer Vision.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: European Conference on Computer Vision

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This paper cites In: Proceedings of the Asian conference on computer vision (2020).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the Asian conference on computer vision (2020)

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This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF international conference on computer vision

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This paper cites Pattern Recognition112, 107706 (2021).

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Pattern Recognition112, 107706 (2021)

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This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Ap- plications of Computer Vision.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF Winter Conference on Ap- plications of Computer Vision

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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This paper cites IEEE Transactions on Neural Net- works and Learning Systems (2024) 18 R.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization IEEE Transactions on Neural Net- works and Learning Systems (2024) 18 R

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