Anomaly Detection Using Generative Adversarial Networks
| Název práce v češtině: | Detekce anomálií pomocí generativních adversariálních sítí |
|---|---|
| Název v anglickém jazyce: | Anomaly Detection Using Generative Adversarial Networks |
| Klíčová slova: | Anomaly detection, generative adversarial networks, neural network, deep learning |
| Klíčová slova anglicky: | Anomaly detection, generative adversarial networks, neural network, deep learning |
| Akademický rok vypsání: | 2018/2019 |
| Typ práce: | bakalářská práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra teoretické informatiky a matematické logiky (32-KTIML) |
| Vedoucí / školitel: | RNDr. Jiří Fink, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 26.10.2018 |
| Datum zadání: | 26.10.2018 |
| Datum potvrzení stud. oddělením: | 31.10.2018 |
| Datum a čas obhajoby: | 27.06.2019 09:00 |
| Datum odevzdání elektronické podoby: | 09.05.2019 |
| Datum odevzdání tištěné podoby: | 17.05.2019 |
| Datum proběhlé obhajoby: | 27.06.2019 |
| Oponenti: | Mgr. Martin Pilát, Ph.D. |
| Zásady pro vypracování |
| Anomaly detection is an important topic in the automotive industry, medical research, computer security and many other areas. Its goal is to identify novelties and anomalies.
Generative adversarial networks (GANs) [1] are unsupervised learning algorithms. They have been used for generating realistic images based on real images in which they achieved state of the art results. An important feature is the ability to learn the distribution of the supplied images. This thesis will exploit this feature for learning the distribution of normal instances and then, during inference, measure how much given instance (possibly anomalous) differs from the learned distribution. The main objective of this work is to study GAN based anomaly detection methods presented in articles [2] and [3] and with the use of Tensorflow framework [4] set suitable parameters for neural network to achieve good results on credit card fraud dataset used in [5] and compare studied methods. Student may try to modify GAN to obtain better results. |
| Seznam odborné literatury |
| [1] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial networks, 2014. arXiv:1406.2661v1
[2] T. Schlegl, P. Seeb ̈ock, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, 2017. arXiv:1703.05921v1 [3] H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar, Efficient GAN-based anomaly detection, 2018. arXiv:1802.06222v1 [4] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man ́e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi ́egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, Software available from https://www.tensorflow.org, 2015. https://www.tensorflow.org/ [5] A. D. Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating Probability with Undersampling for Unbalanced Classification,” in 2015 IEEE Symposium Series on Computational Intelligence, IEEE, 2015 [6] K. G. Mehrotra, C. K. Mohan, and H. Huang, Anomaly Detection Principles and Algorithms. Springer, 2017, isbn:978-3-319-67526-8 |