171 lines
7.6 KiB
BibTeX
Executable File
171 lines
7.6 KiB
BibTeX
Executable File
@article{Chandola2009AnomalyDA,
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title = {Anomaly detection: A survey},
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author = {Varun Chandola and Arindam Banerjee and Vipin Kumar},
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journal = {ACM Comput. Surv.},
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year = {2009},
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volume = {41},
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pages = {15:1-15:58},
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url = {https://api.semanticscholar.org/CorpusID:207172599},
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},
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@dataset{alexander_kyuroson_2023_7913307,
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author = {Alexander Kyuroson and Niklas Dahlquist and Nikolaos Stathoulopoulos
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and Vignesh Kottayam Viswanathan and Anton Koval and George
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Nikolakopoulos},
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title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
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Particles for Frontier Exploration },
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month = may,
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year = 2023,
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publisher = {Zenodo},
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version = {v1},
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doi = {10.5281/zenodo.7913307},
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url = {https://doi.org/10.5281/zenodo.7913307},
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},
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@article{deepsad,
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author = {Lukas Ruff and Robert A. Vandermeulen and Nico G{\"{o}}rnitz and
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Alexander Binder and Emmanuel M{\"{u}}ller and Klaus{-}Robert M{\"{u}
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}ller and Marius Kloft},
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title = {Deep Semi-Supervised Anomaly Detection},
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journal = {CoRR},
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volume = {abs/1906.02694},
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year = {2019},
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url = {http://arxiv.org/abs/1906.02694},
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eprinttype = {arXiv},
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eprint = {1906.02694},
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timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1906-02694.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org},
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},
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@inproceedings{subter,
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title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
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Particles for Frontier Exploration},
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url = {http://dx.doi.org/10.1109/MED59994.2023.10185906},
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DOI = {10.1109/med59994.2023.10185906},
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booktitle = {2023 31st Mediterranean Conference on Control and Automation
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(MED)},
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publisher = {IEEE},
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author = {Kyuroson, Alexander and Dahlquist, Niklas and Stathoulopoulos,
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Nikolaos and Viswanathan, Vignesh Kottayam and Koval, Anton and
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Nikolakopoulos, George},
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year = {2023},
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month = jun,
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pages = {716–721},
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}
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,
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@inproceedings{deepsvdd,
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title = {Deep One-Class Classification},
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author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
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Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
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, Emmanuel and Kloft, Marius},
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booktitle = {Proceedings of the 35th International Conference on Machine
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Learning},
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pages = {4393--4402},
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year = {2018},
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editor = {Dy, Jennifer and Krause, Andreas},
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volume = {80},
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series = {Proceedings of Machine Learning Research},
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month = {10--15 Jul},
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publisher = {PMLR},
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pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
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url = {https://proceedings.mlr.press/v80/ruff18a.html},
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abstract = {Despite the great advances made by deep learning in many machine
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learning problems, there is a relative dearth of deep learning
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approaches for anomaly detection. Those approaches which do exist
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involve networks trained to perform a task other than anomaly
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detection, namely generative models or compression, which are in
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turn adapted for use in anomaly detection; they are not trained on
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an anomaly detection based objective. In this paper we introduce a
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new anomaly detection method—Deep Support Vector Data Description—,
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which is trained on an anomaly detection based objective. The
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adaptation to the deep regime necessitates that our neural network
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and training procedure satisfy certain properties, which we
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demonstrate theoretically. We show the effectiveness of our method
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on MNIST and CIFAR-10 image benchmark datasets as well as on the
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detection of adversarial examples of GTSRB stop signs.},
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},
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@inproceedings{pmlr-v80-ruff18a,
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title = {Deep One-Class Classification},
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author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
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Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
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, Emmanuel and Kloft, Marius},
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booktitle = {Proceedings of the 35th International Conference on Machine
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Learning},
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pages = {4393--4402},
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year = {2018},
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editor = {Dy, Jennifer and Krause, Andreas},
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volume = {80},
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series = {Proceedings of Machine Learning Research},
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month = {10--15 Jul},
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publisher = {PMLR},
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pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
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url = {https://proceedings.mlr.press/v80/ruff18a.html},
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abstract = {Despite the great advances made by deep learning in many machine
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learning problems, there is a relative dearth of deep learning
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approaches for anomaly detection. Those approaches which do exist
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involve networks trained to perform a task other than anomaly
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detection, namely generative models or compression, which are in
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turn adapted for use in anomaly detection; they are not trained on
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an anomaly detection based objective. In this paper we introduce a
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new anomaly detection method—Deep Support Vector Data Description—,
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which is trained on an anomaly detection based objective. The
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adaptation to the deep regime necessitates that our neural network
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and training procedure satisfy certain properties, which we
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demonstrate theoretically. We show the effectiveness of our method
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on MNIST and CIFAR-10 image benchmark datasets as well as on the
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detection of adversarial examples of GTSRB stop signs.},
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},
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@inproceedings{anomaly_detection_medical,
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title = {Anomaly detection for medical images based on a one-class
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classification},
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author = {Qi Wei and Yinhao Ren and Rui Hou and Bibo Shi and Joseph Y. Lo and
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Lawrence Carin},
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booktitle = {Medical Imaging},
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year = {2018},
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url = {https://api.semanticscholar.org/CorpusID:3605439},
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},
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@article{anomaly_detection_defi,
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title = {Anomaly Detection in Blockchain Networks: A Comprehensive Survey},
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author = {Muneeb Ul Hassan and Mubashir Husain Rehmani and Jinjun Chen},
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journal = {IEEE Communications Surveys \& Tutorials},
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year = {2021},
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volume = {25},
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pages = {289-318},
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url = {https://api.semanticscholar.org/CorpusID:245124512},
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},
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@article{anomaly_detection_manufacturing,
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title = {Residual Error Based Anomaly Detection Using Auto-Encoder in SMD
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Machine Sound},
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author = {Dong Yul Oh and Il Dong Yun},
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journal = {Sensors (Basel, Switzerland)},
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year = {2018},
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volume = {18},
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url = {https://api.semanticscholar.org/CorpusID:14006440},
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},
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@article{anomaly_detection_1800s,
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title = {XLI. On discordant observations},
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author = {Francis Ysidro Edgeworth},
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journal = {Philosophical Magazine Series 1},
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year = {1887},
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volume = {23},
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pages = {364-375},
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url = {https://api.semanticscholar.org/CorpusID:120568135},
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},
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@article{degradation_quantification_rain,
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title = {LiDAR Degradation Quantification for Autonomous Driving in Rain},
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author = {Chen Zhang and Zefan Huang and Marcelo H. Ang and Daniela Rus},
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journal = {2021 IEEE/RSJ International Conference on Intelligent Robots and
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Systems (IROS)},
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year = {2021},
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pages = {3458-3464},
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url = {https://api.semanticscholar.org/CorpusID:245264644},
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},
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@article{deep_learning_overview,
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title = {Deep learning in neural networks: An overview},
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author = {J{\"u}rgen Schmidhuber},
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journal = {Neural networks : the official journal of the International Neural
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Network Society},
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year = {2014},
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volume = {61},
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pages = { 85-117 },
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url = {https://api.semanticscholar.org/CorpusID:11715509},
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}
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