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Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection.
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
Hahnloser, R. H.; Sarpeshkar, R.; Mahowald, M. A.; Douglas, R. J.; and Seung, H. S. 2000 · 2000
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge
Landman, B.; Xu, Z.; Igelsias, J.; Styner, M.; Langerak, T.; and Klein, A. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Wide Residual Networks
Zagoruyko, S.; and Komodakis, N. 2016 · 2016
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Instance Normalization: The Missing Ingredient for Fast Stylization
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2017 · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S.; Uchibe, E.; and Doya, K. 2018 · 2018
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MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection
Bergmann, P.; Fauser, M.; Sattlegger, D.; and Steger, C. 2019 · 2019
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
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Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
Yi, J.; and Yoon, S. 2020 · 2020
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Baid, U.; Ghodasara, S.; Mohan, S.; Bilello, M.; Calabrese, E.; Colak, E.; Farahani, K.; Kalpathy-Cramer, J.; Kitamura, F. C.; Pati, S.; et al. 2021 · 2021
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Padim: a patch distribution modeling framework for anomaly detection and localization
Defard, T.; Setkov, A.; Loesch, A.; and Audigier, R. 2021 · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
Li, C.-L.; Sohn, K.; Yoon, J.; and Pfister, T. 2021 · 2021
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Explainable Deep One-Class Classification
Liznerski, P.; Ruff, L.; Vandermeulen, R. A.; Franks, B. J.; Kloft, M.; and Müller, K. 2021 · 2021
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Multiresolution knowledge distillation for anomaly detection
Salehi, M.; Sadjadi, N.; Baselizadeh, S.; Rohban, M. H.; and Rabiee, H. R. 2021 · 2021
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Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2021 · 2021
Cited alongside, same era.
Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions
Tan, D. S.; Chen, Y.-C.; Chen, T. P.-C.; and Chen, W.-C. 2021 · 2021
Cited alongside, same era.
Reconstruction student with attention for student-teacher pyramid matching
Yamada, S.; and Hotta, K. 2021 · 2021
Cited alongside, same era.
Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection
Yan, X.; Zhang, H.; Xu, X.; Hu, X.; and Heng, P. 2021 · 2021
Cited alongside, same era.
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Yu, J.; Zheng, Y.; Wang, X.; Li, W.; Wu, Y.; Zhao, R.; and Wu, L. 2021 · 2021
Cited alongside, same era.
Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey
Tao, X.; Gong, X.; Zhang, X.; Yan, S.; and Adak, C. 2022 · 2022
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AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise
Wyatt, J.; Leach, A.; Schmon, S. M.; and Willcocks, C. G. 2022 · 2022
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Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Yoon, J.; Sohn, K.; Li, C.-L.; Arik, S. O.; Lee, C.-Y.; and Pfister, T. 2022 · 2022
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A Unified Model for Multi-class Anomaly Detection
You, Z.; Cui, L.; Shen, Y.; Yang, K.; Lu, X.; Zheng, Y.; and Le, X. 2022 · 2022
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Eatformer: Improving vision transformer inspired by evolutionary algorithm
Zhang, J.; Li, X.; Wang, Y.; Wang, C.; Yang, Y.; Liu, Y.; and Tao, D. 2022 · 2022
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SegDiff: Image Segmentation with Diffusion Probabilistic Models
Amit, T.; Shaharbany, T.; Nachmani, E.; and Wolf, L. 2022 · 2022
Cited alongside, same era.
The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
Bergmann, P.; Jin, X.; Sattlegger, D.; and Steger, C. 2022 · 2022
Cited alongside, same era.
Informative knowledge distillation for image anomaly segmentation
Cao, Y.; Wan, Q.; Shen, W.; and Gao, L. 2022 · 2022
Cited alongside, same era.
DiffusionDet: Diffusion Model for Object Detection
Chen, S.; Sun, P.; Song, Y.; and Luo, P. 2022 · 2022
Cited alongside, same era.
Anomaly detection via reverse distillation from one-class embedding
Deng, H.; and Li, X. 2022 · 2022
Cited alongside, same era.
Catching both gray and black swans: Open-set supervised anomaly detection
Ding, C.; Pang, G.; and Shen, C. 2022 · 2022
Cited alongside, same era.
Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows
Gudovskiy, D.; Ishizaka, S.; and Kozuka, K. 2022 · 2022
Cited alongside, same era.
Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Zou, Y.; Jeong, J.; Pemula, L.; Zhang, D.; and Dabeer, O. 2022 · 2022
Later among the works it cites.
BMAD: Benchmarks for Medical Anomaly Detection
Bao, J.; Sun, H.; Deng, H.; He, Y.; Zhang, Z.; and Li, X. 2023 · 2023
Closest in time.
The liver tumor segmentation benchmark (lits)
Bilic, P.; Christ, P.; Li, H. B.; Vorontsov, E.; Ben-Cohen, A.; Kaissis, G.; Szeskin, A.; Jacobs, C.; Mamani, G. E. H.; Chartrand, G.; et al. 2023 · 2023
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Segment Any Anomaly without Training via Hybrid Prompt Regularization
Cao, Y.; Xu, X.; Sun, C.; Cheng, Y.; Du, Z.; Gao, L.; and Shen, W. 2023 · 2023
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Chen, X.; Han, Y.; and Zhang, J. 2023 · 2023
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Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Gu, Z.; Liu, L.; Chen, X.; Yi, R.; Zhang, J.; Wang, Y.; Wang, C.; Shu, A.; Jiang, G.; and Ma, L. 2023 · 2023
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Winclip: Zero-/few-shot anomaly classification and segmentation
Jeong, J.; Zou, Y.; Kim, T.; Zhang, D.; Ravichandran, A.; and Dabeer, O. 2023 · 2023
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Omni-frequency channel-selection representations for unsupervised anomaly detection
Liang, Y.; Zhang, J.; Zhao, S.; Wu, R.; Liu, Y.; and Pan, S. 2023 · 2023
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Deep Industrial Image Anomaly Detection: A Survey
Liu, J.; Xie, G.; Wang, J.; Li, S.; Wang, C.; Zheng, F.; and Jin, Y. 2023 · 2023
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Anomaly Detection with Conditioned Denoising Diffusion Models
Mousakhan, A.; Brox, T.; and Tayyub, J. 2023 · 2023
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Multimodal Industrial Anomaly Detection via Hybrid Fusion
Wang, Y.; Peng, J.; Zhang, J.; Yi, R.; Wang, Y.; and Wang, C. 2023 · 2023
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PVG: Progressive Vision Graph for Vision Recognition
Wu, J.; Li, J.; Zhang, J.; Zhang, B.; Chi, M.; Wang, Y.; and Wang, C. 2023 · 2023
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Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Xie, G.; Wang, J.; Liu, J.; Jin, Y.; and Zheng, F. 2023 · 2023
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Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L.; and Agrawala, M. 2023 · 2023
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