%! Author = lukas %! Date = 4/9/24 @InProceedings{crossentropy, ISSN = {00359246}, URL = {http://www.jstor.org/stable/2984087}, 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.}, author = {I. J. Good}, journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, number = {1}, pages = {107--114}, publisher = {[Royal Statistical Society, Wiley]}, title = {Rational Decisions}, urldate = {2024-05-23}, volume = {14}, year = {1952} } @misc{efficientADpaper, title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies}, author={Kilian Batzner and Lars Heckler and Rebecca König}, year={2024}, eprint={2303.14535}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2303.14535}, } @misc{patchcorepaper, title={Towards Total Recall in Industrial Anomaly Detection}, author={Karsten Roth and Latha Pemula and Joaquin Zepeda and Bernhard Schölkopf and Thomas Brox and Peter Gehler}, year={2022}, eprint={2106.08265}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2106.08265}, } @misc{jupyter, author = {}, title = {{Project Jupyter Documentation}}, howpublished = "\url{https://docs.jupyter.org/en/latest/}", year = {2024}, note = "[Online; accessed 13-May-2024]" } @misc{cnnintro, title={An Introduction to Convolutional Neural Networks}, author={Keiron O'Shea and Ryan Nash}, year={2015}, eprint={1511.08458}, archivePrefix={arXiv}, primaryClass={cs.NE} } @misc{cnnarchitectureimg, author = {}, title = {{What are convolutional neural networks?}}, howpublished = "\url{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}", year = {2024}, note = "[Online; accessed 12-April-2024]" } @misc{datasetsampleimg, author = {}, title = {{The MVTec anomaly detection dataset (MVTec AD)}}, howpublished = "\url{https://www.mvtec.com/company/research/datasets/mvtec-ad}", year = {2024}, note = "[Online; accessed 12-April-2024]" } @inproceedings{liang2017soft, title={Soft-margin softmax for deep classification}, author={Liang, Xuezhi and Wang, Xiaobo and Lei, Zhen and Liao, Shengcai and Li, Stan Z}, booktitle={International Conference on Neural Information Processing}, pages={413--421}, year={2017}, organization={Springer} } @inbook{Boltzmann, place = {Cambridge}, series = {Cambridge Library Collection - Physical Sciences}, title = {Studien über das Gleichgewicht der lebendigen Kraft zwischen bewegten materiellen Punkten}, booktitle = {Wissenschaftliche Abhandlungen}, publisher = {Cambridge University Press}, author = {Boltzmann, Ludwig}, editor = {Hasenöhrl, FriedrichEditor}, year = {2012}, pages = {49–96}, collection = {Cambridge Library Collection - Physical Sciences}, key = {value},} @misc{resnet, title={Deep Residual Learning for Image Recognition}, author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, year={2015}, eprint={1512.03385}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{snell2017prototypicalnetworksfewshotlearning, title={Prototypical Networks for Few-shot Learning}, author={Jake Snell and Kevin Swersky and Richard S. Zemel}, year={2017}, eprint={1703.05175}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1703.05175}, } @misc{caml_paper, title={Context-Aware Meta-Learning}, author={Christopher Fifty and Dennis Duan and Ronald G. Junkins and Ehsan Amid and Jure Leskovec and Christopher Re and Sebastian Thrun}, year={2024}, eprint={2310.10971}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2310.10971}, } @misc{handsonaiI, author = {Andreas Schörgenhumer, Bernhard Schäfl, Michael Widrich}, title = {Lecture notes in Hands On AI I, Unit 4 \& 5}, month = {October}, year = {2021}, publisher={Johannes Kepler Universität Linz} } @misc{pmfpaper, title={Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference}, author={Shell Xu Hu and Da Li and Jan Stühmer and Minyoung Kim and Timothy M. Hospedales}, year={2022}, eprint={2204.07305}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2204.07305}, } @misc{peng2023sgvaclipsemanticguidedvisualadapting, title={SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification}, author={Fang Peng and Xiaoshan Yang and Linhui Xiao and Yaowei Wang and Changsheng Xu}, year={2023}, eprint={2211.16191}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2211.16191}, } @misc{singh2022transductivedecoupledvariationalinference, title={Transductive Decoupled Variational Inference for Few-Shot Classification}, author={Anuj Singh and Hadi Jamali-Rad}, year={2022}, eprint={2208.10559}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2208.10559}, } @misc{chen2024unifiedanomalysynthesisstrategy, title={A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization}, author={Qiyu Chen and Huiyuan Luo and Chengkan Lv and Zhengtao Zhang}, year={2024}, eprint={2407.09359}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.09359}, } @misc{shalam2022selfoptimaltransportfeaturetransform, title={The Self-Optimal-Transport Feature Transform}, author={Daniel Shalam and Simon Korman}, year={2022}, eprint={2204.03065}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2204.03065}, } @misc{parnami2022learningexamplessummaryapproaches, title={Learning from Few Examples: A Summary of Approaches to Few-Shot Learning}, author={Archit Parnami and Minwoo Lee}, year={2022}, eprint={2203.04291}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2203.04291}, } @misc{chowdhury2021fewshotimageclassificationjust, title={Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier}, author={Arkabandhu Chowdhury and Mingchao Jiang and Swarat Chaudhuri and Chris Jermaine}, year={2021}, eprint={2101.00562}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2101.00562}, } @book{analysisrudin, title = {Principles of mathematical analysis}, author = {Walter Rudin}, isbn = {}, series = {Mathermatics Series}, year = {1976}, publisher = {Mc Graw Hill}, keywords = {mathematics} } @book{dataminingbook, title = {Data Mining: Concepts and Techniques}, author = {Jiawei Han, Micheline Kamber, Jian Pei}, isbn = {}, series = {The Morgan Kaufmann Series in Data Management Systems}, year = {2012}, publisher = {Morgran Kaufmann}, keywords = {mathematics} } @book{Goodfellow-et-al-2016, title={Deep Learning}, author={Ian Goodfellow and Yoshua Bengio and Aaron Courville}, publisher={MIT Press}, note={\url{http://www.deeplearningbook.org}}, year={2016} }