Abstract
One of the basic ideas for anomaly detection is to describe an enclosing boundary of normal data in order to identify cases outside as anomalies. In practice, however, normal data can consist of multiple classes, in which case the anomalies may appear not only outside such an enclosure but also in-between ‘normal’ classes. This paper addresses deep anomaly detection aimed at embedding ‘normal’ classes to individually close but mutually distant proximities. We introduce a problem setting where a limited number of labeled examples from each ‘normal’ class is available for training. Preparing such examples is much more feasible in practice than collecting examples of anomalies or labeling large-scale, normal data. We utilize the labeled examples in a margin-based loss reflecting the inter-class and the intra-class distances among the embedded labeled data. The two terms and their relations are derived from an information-theoretic principle. In an empirical study using image benchmark datasets, we show the advantage of the proposed method over existing deep anomaly detection models. We also show case studies using low-dimensional mappings to analyze the behavior of the proposed method.
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Notes
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A sample code of the PDD is provided in https://github.com/ProtoDD/pdd.
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References
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39
Alemi, A.A., Fischer, I., Dillon, J.V., Murphy, K.: Deep variational information bottleneck. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)
Ando, S.: Deep representation learning with an information-theoretic loss. CoRR abs/2111.12950 (2021)
Ding, R., Guo, G., Yang, X., Chen, B., Liu, Z., He, X.: BiGAN: collaborative filtering with bidirectional generative adversarial networks. In: Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, pp. 82–90. SIAM (2020)
Ghafoori, Z., Leckie, C.: Deep multi-sphere support vector data description. In: Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, pp. 109–117. SIAM (2020)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. NIPS’14, vol. 2, pp. 2672–2680. MIT Press, Cambridge (2014)
Jeong, T., Kim, H.: OOD-MAML: meta-learning for few-shot out-of-distribution detection and classification. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3907–3916. Curran Associates, Inc. (2020)
Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Clust. Comput. (2017)
Lee, D., Yu, S., Yu, H.: Multi-class data description for out-of-distribution detection. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’20, pp. 1362–1370. Association for Computing Machinery, New York (2020)
McInnes, L., Healy, J., Saul, N., Großberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018)
Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2) (Mar 2021)
Ruff, L., et al.: Deep one-class classification. In: Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393–4402. PMLR (2018)
Ruff, L., et al.: Deep semi-supervised anomaly detection. In: 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net (2020)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54, 45–66 (2004)
Tishby, N., Zaslavsky, N.: Deep learning and the information bottleneck principle. In: 2015 IEEE Information Theory Workshop (ITW), pp. 1–5 (2015)
Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. Comput. Res. Repos. (CoRR) physics/0004057 (2000)
Zenati, H., Romain, M., Foo, C., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727–736 (2018)
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. CoRR abs/1802.06222 (2018),
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection (2019)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms, August 2017
Krizhevsky, A.: Learning multiple layers of features from tiny images. Master’s thesis (2009)
Liu, B., Kang, H., Li, H., Hua, G., Vasconcelos, N.: Few-shot open-set recognition using meta-learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
Jeong, M., Choi, S., Kim, C.: Few-shot open-set recognition by transformation consistency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12566–12575, June 2021
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Ando, S., Yamamoto, A. (2023). Anomaly Detection via Few-Shot Learning on Normality. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_17
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