[65] Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller. A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 2021.
[66] Thomas Schlegl, Philipp Seebock, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging, pages 146-157. Springer, 2017.
[67] Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. Improving robustness against common corruptions by covariate shift adaptation. Advances in Neural Information Processing Systems, 33:11539-11551, 2020.
[68] Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, and Maja Rudolph. Detecting anomalies within time series using local neural transformations. arXiv preprint arXiv:2202.03944, 2022.
[69] Bernhard Scholkopf, Robert C Williamson, Alex Smola, John Shawe-Taylor, and John Platt. Support vector method for novelty detection. Advances in neural information processing systems, 12, 1999.
[70] Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443-1471, 2001.
[71] Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian Scheidegger, Sivan Doveh, and Raja Giryes. Maeday: Mae for few and zero shot anomaly-detection. arXiv preprint arXiv:2211.14307, 2022.
[72] Tom Shenkar and Lior Wolf. Anomaly detection for tabular data with internal contrastive learning. In International Conference on Learning Representations, 2021.
[73] Shelly Sheynin, Sagie Benaim, and Lior Wolf. A hierarchical transformation-discriminating generative model for few shot anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 8495-8504, October 2021.
[74] Yaniv Shulman. Unsupervised contextual anomaly detection using joint deep variational generative models. arXiv preprint arXiv:1904.00548, 2019.
[75] Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister. Learning and evaluating representations for deep one-class classification. In International Conference on Learning Representations, 2020.
[76] Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully test-time adaptation by entropy minimization. In International Conference on Learning Representations, 2021.
[77] Ze Wang, Yipin Zhou, Rui Wang, Tsung-Yu Lin, Ashish Shah, and Ser-Nam Lim. Few-shot fast-adaptive anomaly detection. In Advances in Neural Information Processing Systems, 2022.
[78] Jhih-Ciang Wu, Ding-Jie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu. Learning unsupervised metaformer for anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4369-4378, October 2021.
[79] Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata. Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE transactions on pattern analysis and machine intelligence, 41(9):2251-2265, 2018.
[80] Zehao Xiao, Xiantong Zhen, Shengcai Liao, and Cees GM Snoek. Energy-based test sample adaptation for domain generalization. In The Eleventh International Conference on Learning Representations, 2023.
[81] Jiancheng Yang, Rui Shi, and Bingbing Ni. Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pages 191-195. IEEE, 2021.