[1] C. C. Aggarwal and C. C. Aggarwal. An introduction to outlier analysis. Springer, 2017.
[2] M. Ahmed, A. N. Mahmood, and M. R. Islam. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55:278-288, 2016.
[3] S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon. Ganomaly: Semi-supervised anomaly detection via adversarial training. In ACCV, pages 622–637, 2018.
[4] F. Alimoglu and E. Alpaydin. Methods of combining multiple classifiers based on different representations for pen-based handwritten digit recognition. In TAINN. Citeseer, 1996.
[5] E. Alpaydin and C. Kaynak. Cascading classifiers. Kybernetika, 34(4):369-374, 1998.
[6] I. Ashrapov. Tabular gans for uneven distribution, 2020.
[7] D. Ayres-de Campos, J. Bernardes, A. Garrido, J. Marques-de Sa, and L. Pereira-Leite. Sisporto 2.0: a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine, 2000.
[8] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland. Data mining for credit card fraud: A comparative study. Decision support systems, 50(3):602-613, 2011.
[9] R. J. Bolton and D. J. Hand. Statistical fraud detection: A review. Statistical science, 17(3):235-255, 2002.
[10] N. Brümmer, S. Cumani, O. Glembek, M. Karafiát, P. Matejka, J. Peşan, O. Plchot, M. Soufifar, E. d. Villiers, and J. H. Černocký. Description and analysis of the brno276 system for Ire2011. In Odyssey 2012-the speaker and language recognition workshop, 2012.
[11] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, pages 89–96, 2005.
[12] G. O. Campos, A. Zimek, J. Sander, R. J. Campello, B. Micenkova, E. Schubert, I. Assent, and M. E. Houle. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data mining and knowledge discovery, 30:891-927, 2016.
[13] S. Chauhan and L. Vig. Anomaly detection in ecg time signals via deep long short-term memory networks. In 2015 IEEE international conference on data science and advanced analytics (DSAA), pages 1-7. IEEE, 2015.
[14] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357, 2002.
[15] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[16] T. Dietterich, A. Jain, R. Lathrop, and T. Lozano-Perez. A comparison of dynamic reposing and tangent distance for drug activity prediction. NeurIPS, 6, 1993.
[17] R. Domingues, M. Filippone, P. Michiardi, and J. Zouaoui. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern recognition, 74:406-421, 2018.
[18] M. Du, Z. Chen, C. Liu, R. Oak, and D. Song. Lifelong anomaly detection through unlearning. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 1283-1297, 2019.
[19] A. Emmott, S. Das, T. Dietterich, A. Fern, and W.-K. Wong. A meta-analysis of the anomaly detection problem. ArXiv, 1503.01158, 2015.
[20] S. Y. Feng, V. Gangal, J. Wei, S. Chandar, S. Vosoughi, T. Mitamura, and E. Hovy. A survey of data augmentation approaches for nlp. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 968-988, 2021.
[21] P. W. Frey and D. J. Slate. Letter recognition using holland-style adaptive classifiers. Machine learning, 6(2):161-182, 1991.
[22] J. Gao, X. Song, Q. Wen, P. Wang, L. Sun, and H. Xu. RobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks. KDD Workshop on Mining and Learning from Time Series (KDD-MileTS'20), 2020.