@article{anomaly_detection_survey,
  title = {Anomaly detection: A survey},
  author = {Varun Chandola and Arindam Banerjee and Vipin Kumar},
  journal = {ACM Comput. Surv.},
  year = {2009},
  volume = {41},
  pages = {15:1-15:58},
  url = {https://api.semanticscholar.org/CorpusID:207172599},
},
@dataset{alexander_kyuroson_2023_7913307,
  author = {Alexander Kyuroson and Niklas Dahlquist and Nikolaos Stathoulopoulos
            and Vignesh Kottayam Viswanathan and Anton Koval and George
            Nikolakopoulos},
  title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
           Particles for Frontier Exploration },
  month = may,
  year = 2023,
  publisher = {Zenodo},
  version = {v1},
  doi = {10.5281/zenodo.7913307},
  url = {https://doi.org/10.5281/zenodo.7913307},
},
@article{deepsad,
  author = {Lukas Ruff and Robert A. Vandermeulen and Nico G{\"{o}}rnitz and
            Alexander Binder and Emmanuel M{\"{u}}ller and Klaus{-}Robert M{\"{u}
            }ller and Marius Kloft},
  title = {Deep Semi-Supervised Anomaly Detection},
  journal = {CoRR},
  volume = {abs/1906.02694},
  year = {2019},
  url = {http://arxiv.org/abs/1906.02694},
  eprinttype = {arXiv},
  eprint = {1906.02694},
  timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
  biburl = {https://dblp.org/rec/journals/corr/abs-1906-02694.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
},
@inproceedings{subter,
  title = {Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
           Particles for Frontier Exploration},
  url = {http://dx.doi.org/10.1109/MED59994.2023.10185906},
  DOI = {10.1109/med59994.2023.10185906},
  booktitle = {2023 31st Mediterranean Conference on Control and Automation
               (MED)},
  publisher = {IEEE},
  author = {Kyuroson, Alexander and Dahlquist, Niklas and Stathoulopoulos,
            Nikolaos and Viswanathan, Vignesh Kottayam and Koval, Anton and
            Nikolakopoulos, George},
  year = {2023},
  month = jun,
  pages = {716–721},
}
,
@inproceedings{deepsvdd,
  title = {Deep One-Class Classification},
  author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
            Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
            , Emmanuel and Kloft, Marius},
  booktitle = {Proceedings of the 35th International Conference on Machine
               Learning},
  pages = {4393--4402},
  year = {2018},
  editor = {Dy, Jennifer and Krause, Andreas},
  volume = {80},
  series = {Proceedings of Machine Learning Research},
  month = {10--15 Jul},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
  url = {https://proceedings.mlr.press/v80/ruff18a.html},
  abstract = {Despite the great advances made by deep learning in many machine
              learning problems, there is a relative dearth of deep learning
              approaches for anomaly detection. Those approaches which do exist
              involve networks trained to perform a task other than anomaly
              detection, namely generative models or compression, which are in
              turn adapted for use in anomaly detection; they are not trained on
              an anomaly detection based objective. In this paper we introduce a
              new anomaly detection method—Deep Support Vector Data Description—,
              which is trained on an anomaly detection based objective. The
              adaptation to the deep regime necessitates that our neural network
              and training procedure satisfy certain properties, which we
              demonstrate theoretically. We show the effectiveness of our method
              on MNIST and CIFAR-10 image benchmark datasets as well as on the
              detection of adversarial examples of GTSRB stop signs.},
},
@inproceedings{pmlr-v80-ruff18a,
  title = {Deep One-Class Classification},
  author = {Ruff, Lukas and Vandermeulen, Robert and Goernitz, Nico and Deecke,
            Lucas and Siddiqui, Shoaib Ahmed and Binder, Alexander and M{\"u}ller
            , Emmanuel and Kloft, Marius},
  booktitle = {Proceedings of the 35th International Conference on Machine
               Learning},
  pages = {4393--4402},
  year = {2018},
  editor = {Dy, Jennifer and Krause, Andreas},
  volume = {80},
  series = {Proceedings of Machine Learning Research},
  month = {10--15 Jul},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v80/ruff18a/ruff18a.pdf},
  url = {https://proceedings.mlr.press/v80/ruff18a.html},
  abstract = {Despite the great advances made by deep learning in many machine
              learning problems, there is a relative dearth of deep learning
              approaches for anomaly detection. Those approaches which do exist
              involve networks trained to perform a task other than anomaly
              detection, namely generative models or compression, which are in
              turn adapted for use in anomaly detection; they are not trained on
              an anomaly detection based objective. In this paper we introduce a
              new anomaly detection method—Deep Support Vector Data Description—,
              which is trained on an anomaly detection based objective. The
              adaptation to the deep regime necessitates that our neural network
              and training procedure satisfy certain properties, which we
              demonstrate theoretically. We show the effectiveness of our method
              on MNIST and CIFAR-10 image benchmark datasets as well as on the
              detection of adversarial examples of GTSRB stop signs.},
},
  @inproceedings{anomaly_detection_medical,
  title = {Anomaly detection for medical images based on a one-class
           classification},
  author = {Qi Wei and Yinhao Ren and Rui Hou and Bibo Shi and Joseph Y. Lo and
            Lawrence Carin},
  booktitle = {Medical Imaging},
  year = {2018},
  url = {https://api.semanticscholar.org/CorpusID:3605439},
},
  @article{anomaly_detection_defi,
  title = {Anomaly Detection in Blockchain Networks: A Comprehensive Survey},
  author = {Muneeb Ul Hassan and Mubashir Husain Rehmani and Jinjun Chen},
  journal = {IEEE Communications Surveys \& Tutorials},
  year = {2021},
  volume = {25},
  pages = {289-318},
  url = {https://api.semanticscholar.org/CorpusID:245124512},
},
@article{anomaly_detection_manufacturing,
  title = {Residual Error Based Anomaly Detection Using Auto-Encoder in SMD
           Machine Sound},
  author = {Dong Yul Oh and Il Dong Yun},
  journal = {Sensors (Basel, Switzerland)},
  year = {2018},
  volume = {18},
  url = {https://api.semanticscholar.org/CorpusID:14006440},
},
@article{anomaly_detection_history,
  title = {XLI. On discordant observations},
  author = {Francis Ysidro Edgeworth},
  journal = {Philosophical Magazine Series 1},
  year = {1887},
  volume = {23},
  pages = {364-375},
  url = {https://api.semanticscholar.org/CorpusID:120568135},
},
@article{degradation_quantification_rain,
  title = {LiDAR Degradation Quantification for Autonomous Driving in Rain},
  author = {Chen Zhang and Zefan Huang and Marcelo H. Ang and Daniela Rus},
  journal = {2021 IEEE/RSJ International Conference on Intelligent Robots and
             Systems (IROS)},
  year = {2021},
  pages = {3458-3464},
  url = {https://api.semanticscholar.org/CorpusID:245264644},
},
@article{deep_learning_overview,
  title = {Deep learning in neural networks: An overview},
  author = {J{\"u}rgen Schmidhuber},
  journal = {Neural networks : the official journal of the International Neural
             Network Society},
  year = {2014},
  volume = {61},
  pages = { 85-117 },
  url = {https://api.semanticscholar.org/CorpusID:11715509},
},
@article{autoencoder_survey,
title = {A comprehensive survey on design and application of autoencoder in deep learning},
journal = {Applied Soft Computing},
volume = {138},
pages = {110176},
year = {2023},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2023.110176},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623001941},
author = {Pengzhi Li and Yan Pei and Jianqiang Li},
keywords = {Deep learning, Autoencoder, Unsupervised learning, Feature extraction, Autoencoder application},
abstract = {Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed.}
}
