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111 lines
3.8 KiB
BibTeX
111 lines
3.8 KiB
BibTeX
%! Author = lukas
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%! Date = 4/9/24
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@InProceedings{crossentropy,
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ISSN = {00359246},
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URL = {http://www.jstor.org/stable/2984087},
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abstract = {This paper deals first with the relationship between the theory of probability and the theory of rational behaviour. A method is then suggested for encouraging people to make accurate probability estimates, a connection with the theory of information being mentioned. Finally Wald's theory of statistical decision functions is summarised and generalised and its relation to the theory of rational behaviour is discussed.},
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author = {I. J. Good},
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journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
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number = {1},
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pages = {107--114},
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publisher = {[Royal Statistical Society, Wiley]},
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title = {Rational Decisions},
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urldate = {2024-05-23},
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volume = {14},
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year = {1952}
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}
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@misc{efficientADpaper,
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title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies},
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author={Kilian Batzner and Lars Heckler and Rebecca König},
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year={2024},
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eprint={2303.14535},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2303.14535},
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}
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@misc{patchcorepaper,
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title={Towards Total Recall in Industrial Anomaly Detection},
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author={Karsten Roth and Latha Pemula and Joaquin Zepeda and Bernhard Schölkopf and Thomas Brox and Peter Gehler},
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year={2022},
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eprint={2106.08265},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2106.08265},
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}
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@misc{jupyter,
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author = {},
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title = {{Project Jupyter Documentation}},
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howpublished = "\url{https://docs.jupyter.org/en/latest/}",
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year = {2024},
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note = "[Online; accessed 13-May-2024]"
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}
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@misc{cnnintro,
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title={An Introduction to Convolutional Neural Networks},
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author={Keiron O'Shea and Ryan Nash},
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year={2015},
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eprint={1511.08458},
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archivePrefix={arXiv},
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primaryClass={cs.NE}
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}
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@misc{cnnarchitectureimg,
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author = {},
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title = {{What are convolutional neural networks?}},
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howpublished = "\url{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}",
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year = {2024},
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note = "[Online; accessed 12-April-2024]"
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}
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@misc{datasetsampleimg,
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author = {},
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title = {{The MVTec anomaly detection dataset (MVTec AD)}},
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howpublished = "\url{https://www.mvtec.com/company/research/datasets/mvtec-ad}",
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year = {2024},
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note = "[Online; accessed 12-April-2024]"
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}
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@inproceedings{liang2017soft,
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title={Soft-margin softmax for deep classification},
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author={Liang, Xuezhi and Wang, Xiaobo and Lei, Zhen and Liao, Shengcai and Li, Stan Z},
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booktitle={International Conference on Neural Information Processing},
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pages={413--421},
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year={2017},
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organization={Springer}
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}
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@inbook{Boltzmann,
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place = {Cambridge},
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series = {Cambridge Library Collection - Physical Sciences},
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title = {Studien über das Gleichgewicht der lebendigen Kraft zwischen bewegten materiellen Punkten},
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booktitle = {Wissenschaftliche Abhandlungen},
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publisher = {Cambridge University Press},
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author = {Boltzmann, Ludwig},
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editor = {Hasenöhrl, FriedrichEditor},
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year = {2012},
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pages = {49–96},
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collection = {Cambridge Library Collection - Physical Sciences}, key = {value},}
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@misc{resnet,
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title={Deep Residual Learning for Image Recognition},
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author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
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year={2015},
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eprint={1512.03385},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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@misc{snell2017prototypicalnetworksfewshotlearning,
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title={Prototypical Networks for Few-shot Learning},
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author={Jake Snell and Kevin Swersky and Richard S. Zemel},
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year={2017},
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eprint={1703.05175},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/1703.05175},
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}
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