Abstract
Anomaly detection (AD), a fundamental challenge in machine learning, aims to find samples that do not belong to the distribution of the training data. Among unsupervised anomaly detection models, models based on generative adversarial networks show promising results. These models mainly rely on the rich representations learned from the normal training data to find anomalies. However, their performance is bounded by the limitations of GANs, known as mode collapse, in learning complex training distribution. This work presents a new GAN-based anomaly detection model with a combination of contrastive learning to mitigate the negative effect of mode collapse in more complex distributions. Our unsupervised Anomaly Detection model based on Contrastive Generative Adversarial Network, AD-CGAN, contrasts a sample with local feature maps of itself instead of only contrasting the given sample with other instances as in conventional contrastive learning approaches. Contrastive loss in AD-CGAN helps the model learn more discriminative representations of normal samples. Furthermore, we consider a new normality score to target anomalous samples. The normality score is defined on the encoded representations of samples obtained from the model. Extensive experiments showed AD-CGAN outperforms its counterparts on multiple benchmarks with a significant improvement in ROC-AUC over the previously proposed reconstruction-based approaches.
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References
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture IE 2(1), 1–18 (2015)
Bergman, L., Hoshen, Y.: Classification-based anomaly detection for general data. In: International Conference on Learning Representations (2020)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Chen, T., Zhai, X., Ritter, M., Lucic, M., Houlsby, N.: Self-supervised GANs via auxiliary rotation loss. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12154–12163 (2019)
Cheng, Z., Zhu, E., Wang, S., Zhang, P., Li, W.: Unsupervised outlier detection via transformation invariant autoencoder. IEEE Access 9, 43991–44002 (2021)
Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Anomaly detection with generative adversarial networks (2018). https://openreview.net/forum?id=S1EfylZ0Z
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)
Elson, J., Douceur, J.R., Howell, J., Saul, J.: Asirra: a captcha that exploits interest-aligned manual image categorization. CCS 7, 366–374 (2007)
Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of NIPS, pp. 2672–2680 (2014)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742. IEEE (2006)
Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (2019)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)
Jianliang, M., Haikun, S., Ling, B.: The application on intrusion detection based on k-means cluster algorithm. In: 2009 International Forum on Information Technology and Applications, vol. 1, pp. 150–152. IEEE (2009)
Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GAN. In: International Conference on Learning Representations (2019)
Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C.: Measuring catastrophic forgetting in neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. Master’s thesis, Computer Science Department, University of Toronto (2009)
Latecki, L.J., Lazarevic, A., Pokrajac, D.: Outlier detection with kernel density functions. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 61–75. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73499-4_6
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lee, K.S., Tran, N.T., Cheung, N.M.: Infomax-GAN: improved adversarial image generation via information maximization and contrastive learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3942–3952 (2021)
Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? A large-scale study. In: Proceedings of the NIPS, pp. 700–709 (2018)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065–1076 (1962)
Rafiee, L., Fevens, T.: Unsupervised anomaly detection with a GAN augmented autoencoder. In: Farkaš, I., Masulli, P., Wermter, S. (eds.) ICANN 2020. LNCS, vol. 12396, pp. 479–490. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61609-0_38
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
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al.: Support vector method for novelty detection. In: NIPS, vol. 12, pp. 582–588. Citeseer (1999)
Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)
Tran, N.T., Tran, V.H., Nguyen, B.N., Yang, L., Cheung, N.M.M.: Self-supervised GAN: analysis and improvement with multi-class minimax game. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)
Tran, N.T., Tran, V.H., Nguyen, B.N., Yang, L., Cheung, N.M.M.: Self-supervised GAN: analysis and improvement with multi-class minimax game. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems (2019)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. arXiv preprint arXiv:1802.06222 (2018)
Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727–736. IEEE (2018)
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, pp. 665–674. ACM (2017)
Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
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Sevyeri, L.R., Fevens, T. (2022). AD-CGAN: Contrastive Generative Adversarial Network for Anomaly Detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_27
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