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Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation (2311.14506v2)

Published 24 Nov 2023 in cs.CV

Abstract: In anomaly detection, identification of anomalies across diverse product categories is a complex task. This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process of Coupled-hypersphere-based Feature Adaptation (CFA). By doing so, the proposed Regularized Discriminative Coupled-hypersphere-based Feature Adaptation (RD-CFA), forms a solution for multi-class anomaly detection. By using the discriminative power of RD-VAE to capture intricate class distributions, combined with CFA's robust anomaly detection capability, the proposed method excels in discerning anomalies across various classes. Extensive evaluations on multi-class anomaly detection and localization using the MVTec AD and BeanTech AD datasets showcase the effectiveness of RD-CFA compared to eight leading contemporary methods.

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References (16)
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[2022] You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. 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IEEE Winter Conference on Applications of Computer Vision, 98–107 (2022) Deng and Li [2022] Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. IEEE Conference on Computer Vision and Pattern Recognition, 9737–9746 (2022) You et al. [2022] You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Gudovskiy, D., Ishizaka, S., Kozuka, K.: Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. IEEE Winter Conference on Applications of Computer Vision, 98–107 (2022) Deng and Li [2022] Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. IEEE Conference on Computer Vision and Pattern Recognition, 9737–9746 (2022) You et al. [2022] You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. IEEE Conference on Computer Vision and Pattern Recognition, 9737–9746 (2022) You et al. [2022] You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. 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British Machine Vision Conference (2016) You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. 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[2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. 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[2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. 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[2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. 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British Machine Vision Conference (2016) Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. 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In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. 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In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. 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[2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  5. You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., Le, X.: A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35, 4571–4584 (2022) Kirchheim et al. [2022] Kirchheim, K., Filax, M., Ortmeier, F.: Multi-class hypersphere anomaly detection. In: International Conference on Pattern Recognition, pp. 2636–2642 (2022) Tian et al. [2023] Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. 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[2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Tian, Y., Liu, F., Pang, G., Chen, Y., Liu, Y., Verjans, J.W., Singh, R., Carneiro, G.: Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Medical Image Analysis 90, 102930 (2023) Passalis et al. [2020] Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. 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In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. 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In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  8. Passalis, N., Iosifidis, A., Gabbouj, M., Tefas, A.: Variance-preserving deep metric learning for content-based image retrieval. Pattern Recognition Letters 131, 8–14 (2020) Bergmann et al. [2019] Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  9. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conference on Computer Vision and Pattern Recognition, 9592–9600 (2019) Mishra et al. [2021] Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  10. Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: Vt-adl: A vision transformer network for image anomaly detection and localization. IEEE International Symposium on Industrial Electronics, 01–06 (2021) Yu et al. [2021] Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021) Batzner et al. [2023] Batzner, K., Heckler, L., König, R.: Efficientad: Accurate visual anomaly detection at millisecond-level latencies. arXiv preprint arXiv:2303.14535 (2023) Ahuja et al. [2019] Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. 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  13. Ahuja, N.A., Ndiour, I., Kalyanpur, T., Tickoo, O.: Probabilistic modeling of deep features for out-of-distribution and adversarial detection. stat 1050, 25 (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  14. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. IEEE conference on computer vision and pattern recognition, 248–255 (2009) Lin et al. [2017] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  15. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017) Zagoruyko and Komodakis [2016] Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016) Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
  16. Zagoruyko, S., Komodakis, N.: Wide residual networks. British Machine Vision Conference (2016)
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