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Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Quantum hilbert image scrambling
N. Jiang, L. Wang, and W.-Y. Wu · 2014
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Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger · 2019
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Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings
P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger · 2020
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Sub-image anomaly detection with deep pyramid correspondences
N. Cohen and Y. Hoshen · 2020
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Padim: a patch distribution modeling framework for anomaly detection and localization
T. Defard, A. Setkov, A. Loesch, and R. Audigier · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
C.-L. Li, K. Sohn, J. Yoon, and T. Pfister · 2021
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Same same but differnet: Semi-supervised defect detection with normalizing flows
M. Rudolph, B. Wandt, and B. Rosenhahn · 2021
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Learning semantic context from normal samples for unsupervised anomaly detection
X. Yan, H. Zhang, X. Xu, X. Hu, and P.-A. Heng · 2021
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Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
V. Zavrtanik, M. Kristan, and D. Skočaj · 2021
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Analogous to evolutionary algorithm: Designing a unified sequence model
J. Zhang, C. Xu, J. Li, W. Chen, Y. Wang, Y. Tai, S. Chen, C. Wang, F. Huang, and Y. Liu · 2021
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The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization
P. Bergmann, X. Jin, D. Sattlegger, and C. Steger · 2022
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Informative knowledge distillation for image anomaly segmentation
Y. Cao, Q. Wan, W. Shen, and L. Gao · 2022
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Anomaly detection via reverse distillation from one-class embedding
H. Deng and X. Li · 2022
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Hungry hungry hippos: Towards language modeling with state space models
D. Y. Fu, T. Dao, K. K. Saab, A. W. Thomas, A. Rudra, and C. Ré · 2022
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Long range language modeling via gated state spaces
H. Mehta, A. Gupta, A. Cutkosky, and B. Neyshabur · 2022
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Inpainting transformer for anomaly detection
J. Pirnay and K. Chai · 2022
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Self-supervised predictive convolutional attentive block for anomaly detection
N.-C. Ristea, N. Madan, R. T. Ionescu, K. Nasrollahi, F. S. Khan, T. B. Moeslund, and M. Shah · 2022
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Towards total recall in industrial anomaly detection
K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, and P. Gehler · 2022
Transformer-based visual segmentation: A survey
X. Li, H. Ding, W. Zhang, H. Yuan, G. Cheng, P. Jiangmiao, K. Chen, Z. Liu, and C. C. Loy · 2023
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Omni-frequency channel-selection representations for unsupervised anomaly detection
Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, and S. Pan · 2023
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Simplenet: A simple network for image anomaly detection and localization
Z. Liu, Y. Zhou, Y. Xu, and Z. Wang · 2023
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Bmad: Benchmarks for medical anomaly detection
J. Bao, H. Sun, H. Deng, Y. He, Z. Zhang, and X. Li · 2024
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H. He, J. Zhang, H. Chen, X. Chen, Z. Li, X. Chen, Y. Wang, C. Wang, and L. Xie · 2024
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Localmamba: Visual state space model with windowed selective scan
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H. M. Schlüter, J. Tan, B. Hou, and B. Kainz · 2022
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Simplified state space layers for sequence modeling
J. T. Smith, A. Warrington, and S. W. Linderman · 2022
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Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
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