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Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains.
Deep learning for anomaly detection: A survey, 2019
R. Chalapathy and S. Chawla · 1901
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, and J. C. Platt · 1999
Earlier work this paper cites.
LOF: Identifying density-based local outliers
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander · 2000
Earlier work this paper cites.
Outliers and data descriptions
D. M. Tax and R. P. Duin · 2001
Earlier work this paper cites.
Feature bagging for outlier detection
A. Lazarevic and V. Kumar · 2005
Earlier work this paper cites.
Isolation forest
F. T. Liu, K. M. Ting, and Z.-H. Zhou · 2008
Earlier work this paper cites.
Anomaly detection: A survey
V. Chandola, A. Banerjee, and V. Kumar · 2009
Earlier work this paper cites.
Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
Earlier work this paper cites.
Robust feature learning by stacked autoencoder with maximum correntropy criterion
Y. Qi, Y. Wang, X. Zheng, and Z. Wu · 2014
Earlier work this paper cites.
Theoretical foundations and algorithms for outlier ensembles
C. C. Aggarwal and S. Sathe · 2015
Earlier work this paper cites.
A meta-analysis of the anomaly detection problem
A. Emmott, S. Das, T. Dietterich, A. Fern, and W.-K. Wong · 2015
Earlier work this paper cites.
On the internal evaluation of unsupervised outlier detection
H. O. Marques, R. J. G. B. Campello, A. Zimek, and J. Sander · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Outlier Analysis
C. C. Aggarwal · 2016
Earlier work this paper cites.
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
G. O. Campos, A. Zimek, J. Sander, R. J. G. B. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle · 2016
Earlier work this paper cites.
How to evaluate the quality of unsupervised anomaly detection algorithms?
N. Goix · 2016
Earlier work this paper cites.
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data
M. Goldstein and S. Uchida · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
Cited alongside, same era.
Less is more: Building selective anomaly ensembles
S. Rayana and L. Akoglu · 2016
Cited alongside, same era.
Outlier ensembles: An introduction
C. C. Aggarwal and S. Sathe · 2017
Cited alongside, same era.
Outlier detection with autoencoder ensembles
J. Chen, S. Sathe, C. C. Aggarwal, and D. S. Turaga · 2017
Cited alongside, same era.
Representation Learning on Graphs with Jumping Knowledge Networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
Later among the works it cites.
Efficient gan-based anomaly detection
H. Zenati, C. S. Foo, B. Lecouat, G. Manek, and V. R. Chandrasekhar · 2018
Later among the works it cites.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. ki Cho, and H. Chen · 2018
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f-anogan: Fast unsupervised anomaly detection with generative adversarial networks
T. Schlegl, P. Seeböck, S. M. Waldstein, G. Langs, and U. Schmidt-Erfurth · 2019
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Classification-based anomaly detection for general data
L. Bergman and Y. Hoshen · 2020
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
An evaluation method for unsupervised anomaly detection algorithms
V. Nguyen, T. Nguyen, and U. Nguyen · 2017
Cited alongside, same era.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs · 2017
Cited alongside, same era.
Anomaly detection with robust deep autoencoders
C. Zhou and R. C. Paffenroth · 2017
Cited alongside, same era.
Ganomaly: Semi-supervised anomaly detection via adversarial training
S. Akcay, A. Atapour-Abarghouei, and T. P. Breckon · 2018
Cited alongside, same era.
A. Boyd, R. Bamler, S. Mandt, and P. Smyth · 2020
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Deep multi-sphere support vector data description
Z. Ghafoori and C. Leckie · 2020
Later among the works it cites.
Drocc: Deep robust one-class classification
S. Goyal, A. Raghunathan, M. Jain, H. V. Simhadri, and P. Jain · 2020
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Fleet: Flexible efficient ensemble training for heterogeneous deep neural networks
H. Guan, L. K. Mokadam, X. Shen, S.-H. Lim, and R. M. Patton · 2020
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Hrn: A holistic approach to one class learning
W. Hu, M. Wang, Q. Qin, J. Ma, and B. Liu · 2020
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BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning
Y. Wen, D. Tran, and J. Ba · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
F. Wenzel, J. Snoek, D. Tran, and R. Jenatton · 2020
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Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste · 2021
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Deep learning for anomaly detection: A review
G. Pang, C. Shen, L. Cao, and A. V. D. Hengel · 2021
Later among the works it cites.
A unifying review of deep and shallow anomaly detection
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller · 2021
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Automatic unsupervised outlier model selection
Y. Zhao, R. Rossi, and L. Akoglu · 2021
Later among the works it cites.