N. Meinshausen. Causality from a distributional robustness point of view. In 2018 IEEE Data Science Workshop (DSW), pages 6-10. IEEE, 2018.
Y. Ming, H. Yin, and Y. Li. On the impact of spurious correlation for out-of-distribution detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 10051-10059, 2022.
J. Mitrovic, B. McWilliams, J. Walker, L. Buesing, and C. Blundell. Representation learning via invariant causal mechanisms. arXiv preprint arXiv:2010.07922, 2020.
V. Mothukuri, P. Khare, R. M. Parizi, S. Pouriyeh, A. Dehghantanha, and G. Srivastava. Federated-learning-based anomaly detection for IoT security attacks. IEEE Internet of Things Journal, 9(4): 2545-2554, 2021.
A. T. Nguyen, T. Tran, Y. Gal, and A. G. Baydin. Domain invariant representation learning with domain density transformations. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 5264-5275. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper_files/paper/2021/file/2a2717956118b4d223ceca17ce3865e2-Paper.pdf.
A. v. d. Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
H. Park, J. Noh, and B. Ham. Learning memory-guided normality for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14372-14381, 2020.
D. Ramotsoela, A. Abu-Mahfouz, and G. Hancke. A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study. Sensors, 18(8): 2491, 2018.
T. Reiss and Y. Hoshen. Mean-shifted contrastive loss for anomaly detection. arXiv preprint arXiv:2106.03844, 2021.
T. Reiss, N. Cohen, L. Bergman, and Y. Hoshen. Panda: Adapting pretrained features for anomaly detection and segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2806-2814, 2021.
L. Ruff, J. R. Kauffman, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller. A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5):756-795, 2021.
S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang. Distributionally robust neural networks. In International Conference on Learning Representations, 2019.
T. Schlegl, P. Seebock, S. M. Waldstein, G. Langs, and U. Schmidt-Erfurth. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis, 54:30-44, 2019.
M. A. Siddiqui, J. W. Stokes, C. Seifert, E. Argyle, R. McCann, J. Neil, and J. Carroll. Detecting cyber attacks using anomaly detection with explanations and expert feedback. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2872-2876. IEEE, 2019.
S. Smeu, E. Burceanu, A. L. Nicolicioiu, and E. Haller. Env-aware anomaly detection: Ignore style changes, stay true to content! arXiv preprint arXiv:2210.03103, 2022.
A. Sordoni, N. Dziri, H. Schulz, G. Gordon, P. Bachman, and R. T. Des Combes. Decomposed mutual information estimation for contrastive representation learning. In International Conference on Machine Learning, pages 9859-9869. PMLR, 2021.
J. V. Stone. Independent component analysis: a tutorial introduction. MIT press, 2004.
J. Tack, S. Mo, J. Jeong, and J. Shin. CSI: Novelty detection via contrastive learning on distributionally shifted instances. In Advances in Neural Information Processing Systems, 2020.