Abstract
Anomaly detection is a challenging and fundamental issue in computer vision tasks. In recent years, GAN (Generative Adversarial Networks) based anomaly detection methods have achieved remarkable results. But the instability of training of GAN could be considered that decreases the anomaly detection score. In particular, Bi-directional GAN has the following two causes that make the training difficult: the lack of consistency of the mutual mapping between the image space and the latent space, and the difficulty in conditioning by the latent variables of the image. Here we propose a novel GAN-based anomaly detection model. In our model, we introduce the consistency loss for ensuring mutual mappings. Further, we propose introducing the projection discriminator as an alternative of concatenating discriminator in order to perform efficient conditioning in the Bi-directional GAN model. In experiments, we evaluate the effectiveness of our model in a simple dataset and real-world setting dataset and confirmed that our model outperforms the conventional anomaly detection methods.
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References
Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2016)
Radford, A., Metz, L., Chintala, S.: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In: International Conference on Learning Representations, Workshop (2016)
Di Mattia, F., Galeone, P., De Simoni, M., Ghelfi, E.: A survey on GANs for anomaly detection. arXiv preprint arXiv:1906.11632 (2019)
Wiatrak, M., Albrecht, Stefano, V.: Stabilizing generative adversarial network training: a survey. arXiv preprint arXiv:1910.00927 (2019)
Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. In: International Conference on Learning Representations, Workshop (2018)
Miyato, T., Koyama, M.: cGANs with projection discriminator. In: International Conference on Learning Representations (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., 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
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with Generative Adversarial Networks. Med. Image Anal. 54, 30–44 (2019)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. In: International Conference on Learning Representations (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2642–2651 (2017)
Teterwak, P., et al.: Boundless: Generative Adversarial Networks for image extension. In Proceedings of the IEEE International Conference on Computer Vision, pp. 10521–10530 (2019)
Aich, S., Stavness, I.: Global sum pooling: a generalization trick for object counting with small datasets of large images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 73–82 (2019)
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)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 448–456 (2015)
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for Generative Adversarial Networks. In: International Conference on Learning Representations (2018)
Huang, C., Cao, J., Ye, F., Li, M., Zhang, Y., Lu, C.: Inverse-transform autoencoder for anomaly detection. arXiv preprint arXiv:1911.10676 (2019)
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This work has received funding from Human resource development and research project on production technology for aerospace industry: Subsidy from Gifu Prefecture.
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Komoto, K., Aizawa, H., Kato, K. (2020). Consistency Ensured Bi-directional GAN for Anomaly Detection. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_18
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DOI: https://doi.org/10.1007/978-981-15-4818-5_18
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