- 참고
- [1] Scholkopf, Bernhard, et al. "Estimating the support of a high-dimensional distribution." Neural computation 13.7 (2001): 1443-1471. [2] Parzen, Emanuel. "On estimation of a probability density function and mode." The annals of mathematical statistics 33.3 (1962): 1065-1076. [3] Chalapathy, Raghavendra, and Sanjay Chawla. "Deep learning for anomaly detection: A survey." arXiv preprint arXiv:1901.03407 (2019). [4] Goldstein, Markus, and Seiichi Uchida. "A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data." PloS one 11.4 (2016) [5] An, Jinwon, and Sungzoon Cho. "Variational autoencoder based anomaly detection using reconstruction probability." Special Lecture on IE 2.1 (2015): 1-18. [6] Akcay, Samet, Amir Atapour-Abarghouei, and Toby P. Breckon. "Ganomaly: Semi-supervised anomaly detection via adversarial training." Asian conference on computer vision. Springer, Cham, 2018. [7] Beggel, Laura, Michael Pfeiffer, and Bernd Bischl. "Robust anomaly detection in images using adversarial autoencoders." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2019. [8] Xu, Jiehui, et al. "Anomaly transformer: Time series anomaly detection with association discrepancy." arXiv preprint arXiv:2110.02642 (2021). [9] Lai, Kwei-Herng, et al. "Revisiting time series outlier detection: Definitions and benchmarks." Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). 2021. [10] Dai, Enyan, and Jie Chen. "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series." arXiv preprint arXiv:2202.07857 (2022). [11] https://lilianweng.github.io/posts/2018-10-13-flow-models/
