Abstract
Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset.
This work was partially funded by Beantech srl.
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References
Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481–490 (2019)
Ahmed, M., Mahmood, A.N., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016)
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, Part III. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39
Ambrogioni, L., Güçlü, U., van Gerven, M.A., Maris, E.: The ernel mixture network: A nonparametric method for conditional density estimation of continuous random variables. arXiv preprint arXiv:1705.07111 (2017)
Antonie, M.L., Zaïane, O.R., Coman, A.: Application of data mining techniques for medical image classification. In: Proceedings of the Second International Conference on Multimedia Data Mining, MDMKDD 2001, pp. 94–101. (2001)
Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 37–49 (2012)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD-a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9592–9600 (2019)
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: International joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019)
Bishop, C.M.: Mixture Density Networks. Aston University, Birmingham (1994)
Cai, Q., Pan, Y., Yao, T., Yan, C., Mei, T.: Memory matching networks for one-shot image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4080–4088 (2018)
Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: A survey. CoRR abs/1901.03407 (2019). http://arxiv.org/abs/1901.03407
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 151–1558 (2009)
Chen, P., Yang, S., McCann, J.A.: Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans. Ind. Electron. 62(6), 3832–3842 (2015)
Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Image anomaly detection with generative adversarial networks. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018, Part I. LNCS (LNAI), vol. 11051, pp. 3–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_1
Huang, S.H., Pan, Y.C.: Automated visual inspection in the semiconductor industry: a survey. Comput. Ind. 66, 1–10 (2015)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part II. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)
Klushyn, A., Chen, N., Kurle, R., Cseke, B., van der Smagt, P.: Learning hierarchical priors in VAEs. In: Advances in Neural Information Processing Systems, vol. 32, pp. 2866–2875. Curran Associates, Inc. (2019). http://papers.nips.cc/paper/8553-learning-hierarchical-priors-in-vaes.pdf
Kumagai, A., Iwata, T., Fujiwara, Y.: Transfer anomaly detection by inferring latent domain representations. In: Advances in Neural Information Processing Systems, pp. 2467–2477 (2019)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Mishra, P., Piciarelli, C., Foresti, G.L.: A neural network for image anomaly detection with deep pyramidal representations and dynamic routing. Int. J. Neural Syst. 30(10), 2050060 (2020)
Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18(1), 209 (2018)
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with pixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)
Perera, P., Nallapati, R., Xiang, B.: Ocgan: One-class novelty detection using GANs with constrained latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2898–2906 (2019)
Piciarelli, C., Avola, D., Pannone, D., Foresti, G.L.: A vision-based system for internal pipeline inspection. IEEE Trans. Ind. Inf. 15(6), 3289–3299 (2018)
Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008)
Piciarelli, C., Mishra, P., Foresti, G.L.: Image anomaly detection with capsule networks and imbalanced datasets. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019, Part I. LNCS, vol. 11751, pp. 257–267. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30642-7_23
Pidhorskyi, S., Almohsen, R., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. In: Advances in Neural Information Processing Systems, pp. 6822–6833 (2018)
Qin, X., Cao, L., Rundensteiner, E.A., Madden, S.: Scalable kernel density estimation-based local outlier detection over large data streams. In: EDBT, pp. 421–432 (2019)
Ruff, L., et al.: Deep one-class classification. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393–4402. PMLR, Stockholmsmässan, Stockholm Sweden (2018)
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., Styner, M., Aylward, S., Zhu, H., Oguz, I., Yap, P.-T., Shen, D. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Viroli, C., McLachlan, G.J.: Deep Gaussian mixture models. Stat. Comput. 29(1), 43–51 (2019)
Wulsin, D., Blanco, J., Mani, R., Litt, B.: Semi-supervised anomaly detection for EEG waveforms using deep belief nets. In: 2010 Ninth International Conference on Machine Learning and Applications, pp. 436–441 (2010)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, pp. 665–674. ACM, New York (2017)
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Mishra, P., Piciarelli, C., Foresti, G.L. (2021). Image Anomaly Detection by Aggregating Deep Pyramidal Representations. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_51
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