Fetching the paper…
Reading the bibliography…
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training.
Brown, L.G.: A survey of image registration techniques. ACM computing surveys (CSUR) 24
1992
Earlier work this paper cites.
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a siamese time delay neural network. Advances in neural information processing systems (NeurIPS) 6
1993
Earlier work this paper cites.
Eskin, E.: Anomaly detection over noisy data using learned probability distributions. In: International Conference on Machine Learning (ICML) (2000)
2000
Earlier work this paper cites.
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural computation 13
2001
Earlier work this paper cites.
Zitová, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21
2003
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Peng, H., Chung, P., Long, F., Qu, L., Jenett, A., Seeds, A.M., Myers, E.W., Simpson, J.H.: Brainaligner: 3d registration atlases of drosophila brains. Nature methods 8
2011
Earlier work this paper cites.
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE transactions on pattern analysis and machine intelligence 37
2014
Earlier work this paper cites.
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in neural information processing systems (NeurIPS) 28
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Xia, Y., Cao, X., Wen, F., Hua, G., Sun, J.: Learning discriminative reconstructions for unsupervised outlier removal. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1511–1519 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016)
2016
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning (ICML). pp. 1126–1135 (2017)
2017
Earlier work this paper cites.
Rahmani, M., Atia, G.K.: Coherence pursuit: Fast, simple, and robust principal component analysis. IEEE Transactions on Signal Processing 65
2017
Earlier work this paper cites.
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations (ICLR) (2017)
2017
Earlier work this paper cites.
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging. pp. 146–157. Springer (2017)
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in neural information processing systems (NeurIPS) 30
2017
Cited alongside, same era.
Akçay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Proceedings of the Asian Conference on Computer Vision (ACCV). pp. 622–637. Springer (2018)
2018
Cited alongside, same era.
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1625–1634 (2018)
2018
Cited alongside, same era.
Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Advances in neural information processing systems (NeurIPS). vol. 31 (2018)
2018
Cited alongside, same era.
He, J., Hong, R., Liu, X., Xu, M., Wang, M.: Revisiting deep local descriptor for improved few-shot classification. 30th International Joint Conference on Artificial Intelligence (IJCAI) pp. 3420–3426 (2021)
2021
Later among the works it cites.
Jezek, S., Jonak, M., Burget, R., Dvorak, P., Skotak, M.: Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. In: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). pp. 66–71. IEEE (2021)
2021
Later among the works it cites.
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9664–9674 (2021)
2021
Later among the works it cites.
Rudolph, M., Wandt, B., Rosenhahn, B.: Same same but differnet: Semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 1907–1916 (2021)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matsubara, T., Tachibana, R., Uehara, K.: Anomaly machine component detection by deep generative model with unregularized score. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2018)
2018
Cited alongside, same era.
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., Müller, E., Kloft, M.: Deep one-class classification. In: International Conference on Machine Learning (ICML). pp. 4393–4402 (2018)
2018
Cited alongside, same era.
Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3379–3388 (2018)
2018
Cited alongside, same era.
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1199–1208 (2018)
2018
Cited alongside, same era.
Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (ICLR) (2018)
2018
Cited alongside, same era.
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9592–9600 (2019)
2019
Cited alongside, same era.
Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE Transactions on Image Processing 28
2019
Cited alongside, same era.
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., Hengel, A.v.d.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1705–1714 (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14902–14912 (2021)
2021
Later among the works it cites.
Sheynin, S., Benaim, S., Wolf, L.: A hierarchical transformation-discriminating generative model for few shot anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8495–8504 (2021)
2021
Later among the works it cites.
Wu, J.C., Chen, D.J., Fuh, C.S., Liu, T.L.: Learning unsupervised metaformer for anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4369–4378 (2021)
2021
Later among the works it cites.
Yang, S., Liu, L., Xu, M.: Free lunch for few-shot learning: Distribution calibration. In: International Conference on Learning Representations (ICLR) (2021)
2021
Later among the works it cites.
Zhang, J., Xie, Y., Liao, Z., Pang, G., Verjans, J., Li, W., Sun, Z., He, J., Yi Li, C.S.: Viral pneumonia screening on chest x-ray images using confidence-aware anomaly detection. IEEE transactions on medical imaging 40
2021
Later among the works it cites.
2021
Later among the works it cites.
Gudovskiy, D., Ishizaka, S., Kozuka, K.: Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 98–107 (2022)
2022
Closest in time.
Huang, C., Xu, Q., Wang, Y., Wang, Y., Zhang, Y.: Self-supervised masking for unsupervised anomaly detection and localization. IEEE Transactions on Multimedia (2022)
2022
Closest in time.
Huang, C., Ye, F., Zhao, P., Zhang, Y., Wang, Y., Tian, Q.: Esad: End-to-end semi-supervised anomaly detection. In: The 32nd British Machine Vision Conference (BMVC) (2022)
2022
Closest in time.
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14318–14328 (2022)
2022
Closest in time.
Ye, F., Huang, C., Cao, J., Li, M., Zhang, Y., Lu, C.: Attribute restoration framework for anomaly detection. IEEE Transactions on Multimedia 24
2022
Closest in time.