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Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination.
In: CVPR (2009)
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In: CVPR (2014)
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild · 2014
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In: ECCV (2014)
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context · 2014
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In: ICML (2015)
Rezende, D., Mohamed, S.: Variational inference with normalizing flows · 2015
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In: CVPR (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition · 2016
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In: BMVC (2016)
Zagoruyko, S., Komodakis, N.: Wide residual networks · 2016
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In: ICLR (2017)
Loshchilov, I., Hutter, F.: Stochastic gradient descent with warm restarts · 2017
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In: ACM SIGKDD (2017)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders · 2017
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In: ACCV (2019)
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: Semi-supervised anomaly detection via adversarial training · 2019
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In: CVPR (2019)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection · 2019
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In: CVPR (2019)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks · 2019
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In: ICLR (2019)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization · 2019
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In: CVPR (2020)
Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: How to edit the embedded images? · 2020
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In: CVPR (2020)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings · 2020
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The Visual Computer (2020)
Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile · 2020
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In: CVPR (2020)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan · 2020
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JIM (2020)
Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection · 2020
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In: ECCV (2020)
Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain gan inversion for real image editing · 2020
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In: ICPR (2021)
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization · 2021
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In: CVPR (2021)
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization · 2021
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In: ICCV (2021)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows · 2021
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In: ISIE (2021)
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization · 2021
Cited alongside, same era.
In: CVPR (2021)
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection · 2021
Cited alongside, same era.
ACM TOG (2021)
Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., Cohen-Or, D.: Designing an encoder for stylegan image manipulation · 2021
Cited alongside, same era.
TIE (2021)
Wan, Q., Gao, L., Li, X., Wen, L.: Industrial image anomaly localization based on gaussian clustering of pretrained feature · 2021
Cited alongside, same era.
In: CVPR (2021)
Wang, S., Wu, L., Cui, L., Shen, Y.: Glancing at the patch: Anomaly localization with global and local feature comparison · 2021
Cited alongside, same era.
TIM (2021)
Yan, Y., Wang, D., Zhou, G., Chen, Q.: Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition · 2021
Cited alongside, same era.
In: CVPR (2023)
Lei, J., Hu, X., Wang, Y., Liu, D.: Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow · 2023
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TIP (2023)
Liang, Y., Zhang, J., Zhao, S., Wu, R., Liu, Y., Pan, S.: Omni-frequency channel-selection representations for unsupervised anomaly detection · 2023
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In: CVPR (2023)
Liu, W., Chang, H., Ma, B., Shan, S., Chen, X.: Diversity-measurable anomaly detection · 2023
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In: CVPR (2023)
Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: Simplenet: A simple network for image anomaly detection and localization · 2023
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In: CVPR (2023)
Tien, T.D., Nguyen, A.T., Tran, N.H., Huy, T.D., Duong, S., Nguyen, C.D.T., Truong, S.Q.: Revisiting reverse distillation for anomaly detection · 2023
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TII (2023)
Wan, Q., Gao, L., Li, X., Wen, L.: Unsupervised image anomaly detection and segmentation based on pretrained feature mapping · 2023
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In: ICCV (2021)
Zavrtanik, V., Kristan, M., Skočaj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection · 2021
Cited alongside, same era.
In: CACV (2021)
Zhang, G., Cui, K., Hung, T.Y., Lu, S.: Defect-gan: High-fidelity defect synthesis for automated defect inspection · 2021
Cited alongside, same era.
IJCV (2022)
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization · 2022
Cited alongside, same era.
In: VISIGRAPP (2022)
Bergmann, P., Jin, X., Sattlegger, D., Steger, C.: The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization · 2022
Cited alongside, same era.
KBS (2022)
Cao, Y., Wan, Q., Shen, W., Gao, L.: Informative knowledge distillation for image anomaly segmentation · 2022
Cited alongside, same era.
In: CVPR (2022)
Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding · 2022
Cited alongside, same era.
Wang, R., Hoppe, S., Monari, E., Huber, M.F.: Defect transfer gan: diverse defect synthesis for data augmentation · 2023
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arXiv preprint arXiv:2312.07495 (2023)
Zhang, J., Chen, X., Wang, Y., Wang, C., Liu, Y., Li, X., Yang, M.H., Tao, D.: Exploring plain vit reconstruction for multi-class unsupervised anomaly detection · 2023
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In: ICCV (2023)
Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., Wang, C.: Rethinking mobile block for efficient attention-based models · 2023
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In: CVPR (2023)
Zhang, X., Li, S., Li, X., Huang, P., Shan, J., Chen, T.: Destseg: Segmentation guided denoising student-teacher for anomaly detection · 2023
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arXiv preprint arXiv:2401.16402 (2024)
Cao, Y., Xu, X., Zhang, J., Cheng, Y., Huang, X., Pang, G., Shen, W.: A survey on visual anomaly detection: Challenge, approach, and prospect · 2024
Closest in time.
arXiv (2024)
He, H., Bai, Y., Zhang, J., He, Q., Chen, H., Gan, Z., Wang, C., Li, X., Tian, G., Xie, L.: Mambaad: Exploring state space models for multi-class unsupervised anomaly detection · 2024
Closest in time.
In: AAAI (2024)
He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., Xie, L.: A diffusion-based framework for multi-class anomaly detection · 2024
Closest in time.
In: AAAI (2024)
Hu, T., Zhang, J., Yi, R., Du, Y., Chen, X., Liu, L., Wang, Y., Wang, C.: Anomalydiffusion: Few-shot anomaly image generation with diffusion model · 2024
Closest in time.
NeurIPS (2024)
Liu, J., Xie, G., Chen, R., Li, X., Wang, J., Liu, Y., Wang, C., Zheng, F.: Real3d-ad: A dataset of point cloud anomaly detection · 2024
Closest in time.
MIR (2024)
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., Jin, Y.: Deep industrial image anomaly detection: A survey · 2024
Closest in time.
In: CVPR (2024)
Wang, C., Zhu, W., Gao, B.B., Gan, Z., Zhang, J., Gu, Z., Qian, S., Chen, M., Ma, L.: Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection · 2024
Closest in time.
T-PAMI (2024)
Wu, J., Li, X., Xu, S., Yuan, H., Ding, H., Yang, Y., Li, X., Zhang, J., Tong, Y., Jiang, X., Ghanem, B., Tao, D.: Towards open vocabulary learning: A survey · 2024
Closest in time.
IJCV (2024)
Zhang, J., Li, X., Wang, Y., Wang, C., Yang, Y., Liu, Y., Tao, D.: Eatformer: Improving vision transformer inspired by evolutionary algorithm · 2024
Closest in time.
NeurIPS (2024)
Zhou, Q., Li, W., Jiang, L., Wang, G., Zhou, G., Zhang, S., Zhao, H.: Pad: A dataset and benchmark for pose-agnostic anomaly detection · 2024
Closest in time.
arXiv preprint arXiv:2403.06495 (2024)
Zhu, J., Pang, G.: Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts · 2024
Closest in time.