thesis-camera-trap-anomaly-detection/thesis.bib

% introduction

@article{Cardinale12:BiodiversityLoss,
author = {Cardinale, Bradley and Duffy, J. and Gonzalez, Andrew and Hooper, David and Perrings, Charles and Venail, Patrick and Narwani, Anita and Tilman, David and Wardle, David and Kinzig, Ann and Daily, Gretchen and Loreau, Michel and Grace, James and Larigauderie, Anne and Srivastava, Diane and Naeem, Shahid},
year = {2012},
month = {06},
pages = {59-67},
title = {Biodiversity loss and its impact on humanity},
volume = {486},
journal = {Nature},
doi = {10.1038/nature11148}
}


@Article{Bianchi22:BiodiversityMonitoring,
AUTHOR = {Bianchi, Carlo Nike and Azzola, Annalisa and Cocito, Silvia and Morri, Carla and Oprandi, Alice and Peirano, Andrea and Sgorbini, Sergio and Montefalcone, Monica},
TITLE = {Biodiversity Monitoring in Mediterranean Marine Protected Areas: Scientific and Methodological Challenges},
JOURNAL = {Diversity},
VOLUME = {14},
YEAR = {2022},
NUMBER = {1},
ARTICLE-NUMBER = {43},
URL = {https://www.mdpi.com/1424-2818/14/1/43},
ISSN = {1424-2818},
DOI = {10.3390/d14010043}
}

% related work

@article{Collins00:VideoSurveillance,
author = {Collins, Robert and Lipton, Alan and Kanade, Takeo and Fujiyoshi, Hironobu and Duggins, David and Tsin, Yanghai and Tolliver, David and Enomoto, Nobuyoshi and Hasegawa, Osamu and Burt, Peter},
year = {2000},
month = {06},
pages = {},
title = {A System for Video Surveillance and Monitoring},
volume = {5},
journal = {Robot. Inst.}
}

@inproceedings{Gupta07:FrameDifferencing,
author = {Gupta, Karan and Kulkarni, Anjali},
year = {2007},
month = {01},
pages = {245-250},
title = {Implementation of an Automated Single Camera Object Tracking System Using Frame Differencing and Dynamic Template Matching},
isbn = {978-1-4020-8740-0},
doi = {10.1007/978-1-4020-8741-7_44}
}

@misc{Lis19:ADImageResynthesis,
  doi = {10.48550/ARXIV.1904.07595},
  
  url = {https://arxiv.org/abs/1904.07595},
  
  author = {Lis, Krzysztof and Nakka, Krishna and Fua, Pascal and Salzmann, Mathieu},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.4.6; I.4.8},
  
  title = {Detecting the Unexpected via Image Resynthesis},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@misc{DiBiase21:PixelwiseAD,
  doi = {10.48550/ARXIV.2103.05445},
  
  url = {https://arxiv.org/abs/2103.05445},
  
  author = {Di Biase, Giancarlo and Blum, Hermann and Siegwart, Roland and Cadena, Cesar},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Pixel-wise Anomaly Detection in Complex Driving Scenes},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Japkowicz99:FirstAE,
author = {Japkowicz, Nathalie and Myers, Catherine and Gluck, Mark},
year = {1999},
month = {10},
pages = {},
title = {A Novelty Detection Approach to Classification},
journal = {Proceedings of the Fourteenth Joint Conference on Artificial Intelligence}
}

@article{Perera19:DeepOCC,
	doi = {10.1109/tip.2019.2917862},
	url = {https://doi.org/10.1109%2Ftip.2019.2917862},
	year = 2019,
	month = {nov},
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
	volume = {28},
	number = {11},
	pages = {5450--5463},
	author = {Pramuditha Perera and Vishal M. Patel},
	title = {Learning Deep Features for One-Class Classification},
	journal = {{IEEE} Transactions on Image Processing}
}

@article{LeCun15:DeepLearning,
author = {LeCun, Yann and Bengio, Y. and Hinton, Geoffrey},
year = {2015},
month = {05},
pages = {436-44},
title = {Deep Learning},
volume = {521},
journal = {Nature},
doi = {10.1038/nature14539}
}

@article{LeCun89:CNN,
title={Backpropagation applied to handwritten zip code recognition},
author={LeCun, Yann and Boser, Bernhard and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne and Jackel, Lawrence D},
journal={Neural computation},
volume={1},
number={4},
pages={541--551},
year={1989},
publisher={MIT Press}
}

@article{Oza19:OCCNN,
	doi = {10.1109/lsp.2018.2889273},
	url = {https://doi.org/10.1109%2Flsp.2018.2889273},
	year = 2019,
	month = {feb},
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
	volume = {26},
	number = {2},
	pages = {277--281},
	author = {Poojan Oza and Vishal M. Patel},
	title = {One-Class Convolutional Neural Network},
	journal = {{IEEE} Signal Processing Letters}
}

% theory

@inbook{Kecman05:SVMs,
  author = {Kecman, Vojislav},
  year = {2005},
  month = {05},
  pages = {605--605},
  title = {Support Vector Machines - An Introduction},
  volume = {177},
  isbn = {978-3-540-24388-5},
  journal = {Support Vector Machines: Theory and Applications},
  doi = {10.1007/10984697_1}
}

@article{Gidudu:SVMsMultiClass,
  author = {Gidudu, Anthony and Hulley, Gregory and Marwala, Tshilidzi},
  year = {2007},
  month = {11},
  pages = {},
  title = {Image classification using SVMs: One-Against-One Vs One-against-All},
  volume = {abs/0711.2914},
  journal = {CoRR}
}

@article{Chang10:RBF,
  author  = {Yin-Wen Chang and Cho-Jui Hsieh and Kai-Wei Chang and Michael Ringgaard and Chih-Jen Lin},
  title   = {Training and Testing Low-degree Polynomial Data Mappings via Linear SVM},
  journal = {Journal of Machine Learning Research},
  year    = {2010},
  volume  = {11},
  number  = {48},
  pages   = {1471--1490},
  url     = {http://jmlr.org/papers/v11/chang10a.html}
}

@misc{Chalapathy19:DeepLearningADSurvey,
  doi = {10.48550/ARXIV.1901.03407},
  url = {https://arxiv.org/abs/1901.03407},
  author = {Chalapathy, Raghavendra and Chawla, Sanjay},
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Deep Learning for Anomaly Detection: A Survey},
  publisher = {arXiv},
  year = {2019},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@Article{Kiran18:ADInVideo,
  AUTHOR = {Kiran, B. Ravi and Thomas, Dilip Mathew and Parakkal, Ranjith},
  TITLE = {An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos},
  JOURNAL = {Journal of Imaging},
  VOLUME = {4},
  YEAR = {2018},
  NUMBER = {2},
  ARTICLE-NUMBER = {36},
  URL = {https://www.mdpi.com/2313-433X/4/2/36},
  ISSN = {2313-433X},
  DOI = {10.3390/jimaging4020036}
}

@misc{Jiang22:VisualSensoryADSurvey,
  doi = {10.48550/ARXIV.2202.07006},
  url = {https://arxiv.org/abs/2202.07006},
  author = {Jiang, Xi and Xie, Guoyang and Wang, Jinbao and Liu, Yong and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {A Survey of Visual Sensory Anomaly Detection},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}

@article{Perera21:OCCSurvey,
  title={One-Class Classification: A Survey},
  author={Pramuditha Perera and Poojan Oza and Vishal M. Patel},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.03064}
}

@article{Rosenblatt56:KDE1,
author = {Murray Rosenblatt},
title = {{Remarks on Some Nonparametric Estimates of a Density Function}},
volume = {27},
journal = {The Annals of Mathematical Statistics},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {832 -- 837},
year = {1956},
doi = {10.1214/aoms/1177728190},
URL = {https://doi.org/10.1214/aoms/1177728190}
}

@article{Parzen62:KDE2,
author = {Emanuel Parzen},
title = {{On Estimation of a Probability Density Function and Mode}},
volume = {33},
journal = {The Annals of Mathematical Statistics},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {1065 -- 1076},
year = {1962},
doi = {10.1214/aoms/1177704472},
URL = {https://doi.org/10.1214/aoms/1177704472}
}

@book{Goodfellow16:DeepLearning,
  title={Deep Learning},
  author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher={MIT Press},
  note={\url{http://www.deeplearningbook.org}},
  year={2016}
}

@inproceedings{Nair10:ReLU,
  author = {Nair, Vinod and Hinton, Geoffrey},
  year = {2010},
  month = {06},
  pages = {807-814},
  title = {Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair},
  volume = {27},
  journal = {Proceedings of ICML}
}

@article{Bank20:Autoencoders,
  title={Autoencoders},
  author={Dor Bank and Noam Koenigstein and Raja Giryes},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.05991}
}

@inproceedings{Rumelhart86:Autoencoders,
  title={Learning internal representations by error propagation},
  author={David E. Rumelhart and Geoffrey E. Hinton and Ronald J. Williams},
  year={1986}
}

@misc{Butt20:FrameDifferencing,
  doi = {10.48550/ARXIV.2012.10708},
  url = {https://arxiv.org/abs/2012.10708},
  author = {Butt, Waqqas-ur-Rehman and Servin, Martin},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Static object detection and segmentation in videos based on dual foregrounds difference with noise filtering},
  publisher = {arXiv},
  year = {2020},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Tekeli19:EliminationOfUselessImages,
  author = {TEKELİ, Ulaş and Bastanlar, Yalin},
  year = {2019},
  month = {07},
  pages = {2395-2411},
  title = {Elimination of useless images from raw camera-trap data},
  volume = {27},
  journal = {TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES},
  doi = {10.3906/elk-1808-130}
}

@article{Hinton06:Autoencoders,
author = {G. E. Hinton  and R. R. Salakhutdinov },
title = {Reducing the Dimensionality of Data with Neural Networks},
journal = {Science},
volume = {313},
number = {5786},
pages = {504-507},
year = {2006},
doi = {10.1126/science.1127647},
URL = {https://www.science.org/doi/abs/10.1126/science.1127647},
eprint = {https://www.science.org/doi/pdf/10.1126/science.1127647},
abstract = {High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.}}

@inproceedings{Schoelkopf99:OneClassSVM,
 author = {Sch\"{o}lkopf, Bernhard and Williamson, Robert C and Smola, Alex and Shawe-Taylor, John and Platt, John},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Solla and T. Leen and K. M\"{u}ller},
 pages = {},
 publisher = {MIT Press},
 title = {Support Vector Method for Novelty Detection},
 url = {https://proceedings.neurips.cc/paper/1999/file/8725fb777f25776ffa9076e44fcfd776-Paper.pdf},
 volume = {12},
 year = {1999}
}

@misc{Yang21:OODSurvey,
  doi = {10.48550/ARXIV.2110.11334},
  url = {https://arxiv.org/abs/2110.11334},
  author = {Yang, Jingkang and Zhou, Kaiyang and Li, Yixuan and Liu, Ziwei},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Generalized Out-of-Distribution Detection: A Survey},
  publisher = {arXiv},
  year = {2021},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@book{Silverman86:DensityEstimation,
  title={Density estimation for statistics and data analysis},
  author={Silverman, Bernard W},
  year={1986},
  publisher={Chapman and Hall}
}

@article{Pimentel14:NoveltyDetection,
  title = {A review of novelty detection},
  journal = {Signal Processing},
  volume = {99},
  pages = {215-249},
  year = {2014},
  issn = {0165-1684},
  doi = {https://doi.org/10.1016/j.sigpro.2013.12.026},
  url = {https://www.sciencedirect.com/science/article/pii/S016516841300515X},
  author = {Marco A.F. Pimentel and David A. Clifton and Lei Clifton and Lionel Tarassenko},
  keywords = {Novelty detection, One-class classification, Machine learning},
  abstract = {Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as “one-class classification”, in which a model is constructed to describe “normal” training data. The novelty detection approach is typically used when the quantity of available “abnormal” data is insufficient to construct explicit models for non-normal classes. Application includes inference in datasets from critical systems, where the quantity of available normal data is very large, such that “normality” may be accurately modelled. In this review we aim to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.}
}

@article{Lowe04:SIFT,
author = {Lowe, David},
year = {2004},
month = {11},
pages = {91-},
title = {Distinctive Image Features from Scale-Invariant Keypoints},
volume = {60},
journal = {International Journal of Computer Vision},
doi = {10.1023/B:VISI.0000029664.99615.94}
}

@book{Chavez12:DSIFT,
  title={Image classification with dense SIFT sampling: an exploration of optimal parameters},
  author={Chavez, Aaron J},
  year={2012},
  publisher={Kansas State University}
}

@InProceedings{Bosch06:DSIFT1,
  author="Bosch, Anna and Zisserman, Andrew and Mu{\~{n}}oz, Xavier",
  editor="Leonardis, Ale{\v{s}} and Bischof, Horst and Pinz, Axel",
  title="Scene Classification Via pLSA",
  booktitle="Computer Vision -- ECCV 2006",
  year="2006",
  publisher="Springer Berlin Heidelberg",
  address="Berlin, Heidelberg",
  pages="517--530",
  isbn="978-3-540-33839-0"
}

@INPROCEEDINGS{Bosch07:DSIFT2,
  author={Bosch, Anna and Zisserman, Andrew and Mu{\~{n}}oz, Xavier},
  booktitle={2007 IEEE 11th International Conference on Computer Vision},
  title={Image Classification using Random Forests and Ferns},
  year={2007},
  volume={},
  number={},
  pages={1-8},
  doi={10.1109/ICCV.2007.4409066}
}

@inproceedings{Tuytelaars10:DenseInterestPoints,
author = {Tuytelaars, Tinne},
year = {2010},
month = {06},
pages = {2281-2288},
title = {Dense Interest Points},
doi = {10.1109/CVPR.2010.5539911}
}

@article{Ng11:SparseAutoencoder,
  title={Sparse autoencoder},
  author={Ng, Andrew and others},
  journal={CS294A Lecture notes},
  volume={72},
  number={2011},
  pages={1--19},
  year={2011},
  url={https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf}
}

@article{Majnik13:ROCAnalysis,
author = {Majnik, Matjaž and Bosnic, Zoran},
year = {2013},
month = {05},
pages = {531-558},
title = {ROC analysis of classifiers in machine learning: A survey},
volume = {17},
journal = {Intelligent Data Analysis},
doi = {10.3233/IDA-130592}
}

@article{Flach05:ROCCurves,
author = {Flach, Peter and Wu, Shaomin},
year = {2005},
month = {01},
pages = {},
title = {Repairing Concavities in ROC Curves},
journal = {Reading}
}

@article{Hodge04:OutlierDetectionSurvey,
author = {Hodge, Victoria and Austin, Jim},
year = {2004},
month = {10},
pages = {85-126},
title = {A Survey of Outlier Detection Methodologies},
volume = {22},
journal = {Artificial Intelligence Review},
doi = {10.1023/B:AIRE.0000045502.10941.a9}
}

=== Already existing solutions ===

@article{Norouzzadeh18:Solution1,
author = {Mohammad Sadegh Norouzzadeh  and Anh Nguyen  and Margaret Kosmala  and Alexandra Swanson  and Meredith S. Palmer  and Craig Packer  and Jeff Clune },
title = {Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning},
journal = {Proceedings of the National Academy of Sciences},
volume = {115},
number = {25},
pages = {E5716-E5725},
year = {2018},
doi = {10.1073/pnas.1719367115},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1719367115},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1719367115}}

@article{Willi19:Solution2,
author = {Willi, Marco and Pitman, Ross T. and Cardoso, Anabelle W. and Locke, Christina and Swanson, Alexandra and Boyer, Amy and Veldthuis, Marten and Fortson, Lucy},
title = {Identifying animal species in camera trap images using deep learning and citizen science},
journal = {Methods in Ecology and Evolution},
volume = {10},
number = {1},
pages = {80-91},
keywords = {animal identification, camera trap, citizen science, convolutional neural networks, deep learning, machine learning},
doi = {https://doi.org/10.1111/2041-210X.13099},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13099},
eprint = {https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13099},
year = {2019}
}

@article{Yang21:SolutionEnsemble,
author = {Yang, Deng-Qi and Tan, Kun and Huang, Zhi-Pang and Li, Xiao-Wei and Chen, Ben-Hui and Ren, Guo-Peng and Xiao, Wen},
title = {An automatic method for removing empty camera trap images using ensemble learning},
journal = {Ecology and Evolution},
volume = {11},
number = {12},
pages = {7591-7601},
keywords = {artificial intelligence, camera trap images, convolutional neural networks, deep learning, ensemble learning},
doi = {https://doi.org/10.1002/ece3.7591},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.7591},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.7591},
year = {2021}
}

% Experiments

@misc{Kingma14:Adam,
  doi = {10.48550/ARXIV.1412.6980},
  
  url = {https://arxiv.org/abs/1412.6980},
  
  author = {Kingma, Diederik P. and Ba, Jimmy},
  
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Adam: A Method for Stochastic Optimization},
  
  publisher = {arXiv},
  
  year = {2014},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@conference{Kluyver16:Jupyter,
Title = {Jupyter Notebooks -- a publishing format for reproducible computational workflows},
Author = {Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing},
Booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas},
Editor = {F. Loizides and B. Schmidt},
Organization = {IOS Press},
Pages = {87 - 90},
Year = {2016}
}

@Article{Harris20:NumPy,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

@article{Bradski00:OpenCV,
    author = {Bradski, G.},
    citeulike-article-id = {2236121},
    journal = {Dr. Dobb's Journal of Software Tools},
    keywords = {bibtex-import},
    posted-at = {2008-01-15 19:21:54},
    priority = {4},
    title = {{The OpenCV Library}},
    year = {2000}
}

@article{Pedregosa11:scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@Article{Hunter07:Matplotlib,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}

@incollection{Paszke19:PyTorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

% Further work

@inproceedings{Haensch14:ColorSpacesForGraphCut,
author = {H{\"a}nsch, Ronny and Wang, Xi and Hellwich, Olaf},
year = {2014},
month = {01},
pages = {},
title = {Comparison of different Color Spaces for Image Segmentation using Graph-Cut},
volume = {1},
journal = {VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications}
}

@article{Xu18:VAEforAD,
  author    = {Haowen Xu and
               Wenxiao Chen and
               Nengwen Zhao and
               Zeyan Li and
               Jiahao Bu and
               Zhihan Li and
               Ying Liu and
               Youjian Zhao and
               Dan Pei and
               Yang Feng and
               Jie Chen and
               Zhaogang Wang and
               Honglin Qiao},
  title     = {Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal
               KPIs in Web Applications},
  journal   = {CoRR},
  volume    = {abs/1802.03903},
  year      = {2018},
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