[1] Pang Guansong, Shen Chunhua, Cao Longbing, et al. Deep learning for anomaly detection:a review[J]. ACM Computing Surveys, 2021, 54(2):1-38. [2] Ribeiro M, Lazzaretti A E, Lopes H S. A study of deep convolutional auto-encoders for anomaly detection in videos[J]. Pattern Recognition Letters, 2018, 105:13-22. [3] Zhou Chong, Paffenroth R C. Anomaly detection with robust deep autoencoders[C]//23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax:ACM, 2017:665-674. [4] Zong Bo, Song Qi, Min M R, et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations. Vancouver:IEEE, 2018:1-19. [5] An J, Cho S. Variational autoencoder based anomaly detection using reconstruction probability[J]. Special Lecture on IE, 2015, 2(1):1-18. [6] Yao Rong, Liu Chongdang, Zhang Linxuan, et al. Unsupervised anomaly detection using variational auto-encoder based feature extraction[C]//2019 IEEE International Conference on Prognostics and Health Management (ICPHM). San Francisco:IEEE, 2019:1-7. [7] Schlegl T, Seeböck P, Waldstein S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]//International Conference on Information Processing in Medical Imaging. Boone:Springer Cham, 2017:146-157. [8] Zenati H, Romain M, Foo C S, et al. Adversarially learned anomaly detection[C]//2018 IEEE International Conference on Data Mining (ICDM). Singapore:IEEE, 2018:727-736. [9] Li Chunyuan, Liu Hao, Chen Changyou, et al. ALICE:towards understanding adversarial learning for joint distribution matching[C]//Advances in Neural Information Processing Systems. Long Beach:MIT, 2017:5495-5503. [10] Pidhorskyi S, Almohsen R, Doretto G. Generative pro-babilistic novelty detection with adversarial autoencoders[C]//Advances in Neural Information Processing Systems. Montréal:MIT, 2018:6822-6833. [11] Ruff L, Vandermeulen R A, Görnitz N, et al. Deep semi-supervised anomaly detection[C]//International Conference on Learning Representations. Addis Ababa:IEEE, 2020:1-13. [12] Crescimanna V, Graham B. An information theoretic approach to the autoencoder[C]//INNS Big Data and Deep Learning Conference. Genoa:Springer Cham, 2019:99-108. [13] Belghazi M I, Baratin A, Rajeshwar S, et al. Mutual information neural estimation[C]//International Confe-rence on Machine Learning. Stockholm:PMLR, 2018:531-540. [14] Rezaabad A L, Vishwanath S. Learning representations by maximizing mutual information in variational autoencoders[C]//2020 IEEE International Symposium on Information Theory (ISIT). Los Angeles:IEEE, 2020:2729-2734. [15] Marco S. Financial fraud detection dataset:version2[EB/OL]. (2018-05-11)[2021-01-15]. https://github.com/gitiHubi/deepAD. [16] Shebuti R. Outlier detection datasets[EB/OL]. (2016-01-01)[2021-01-15]. New York:Stony Brook University. http://odds.cs.stonybrook.edu. [17] Erfani S M, Rajasegarar S, Karunasekera S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016, 58:121-134. |