Abstract
People quickly and efficiently take in visually processed information. The processing of data of any kind is therefore an important and heavily researched area in data science and all its surrounding fields. The aim is to simplify complex information as much as possible so that the core information can be transported as simply and clearly as possible without significant loss of meaning. In turn, there is an increasing need to automatically process images and image information, whether for facial recognition as a biometric feature, for personal assistants, or for the evaluation of camera images in driverless cars. The article shows what possibilities exist in each case and how algorithms, e.g. in deep learning, can train and improve themselves or each other.
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References
Brell C, Brell J, Kirsch S (2016) Statistik von Null auf Hundert, 2. Aufl. Springer, Berlin
Kahl T, Zimmer F (Hrsg) (2020) Interaktive Datenvisualisierung in Wissenschaft und Unternehmenspraxis. Springer, Wiesbaden
Davies J (2020) Word cloud generator. https://www.jasondavies.com/wordcloud/. Accessed: 8. Dec 2020
Provost F, Fawcett T (2017) Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly and Associates
OpenGeoDB Downloads (2020) OpenGeoDb, Die freie Geoinformatik-Wissensdatenbank. http://opengeodb.giswii.org/index.php?title=OpenGeoDB_Downloads&oldid=13822. Accessed: 8. Dec 2020
Backhaus K, Erichson B, Plinke W, Weiber R (2018) Multivariate analysemethoden, 15th edn. Springer, Berlin
Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(2008):2579–2605
Wattenberg et al (2016) How to use t-SNE effectively. Distill. https://doi.org/10.23915/distill.00002
Bitkom e. V. (2018) Digitalisierung gestalten mit dem Periodensystem der Künstlichen Intelligenz. Ein Navigationssystem für Entscheider. Hg. v. Bitkom e. V. Berlin. https://www.bitkom.org/sites/default/files/2018-12/181204_LF_Periodensystem_online_0.pdf
Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics 20 (Suppl 11): 281. https://doi.org/10.1186/s12859-019-2823-4
Ding Y, Sohn JH, Kawczynski MG et al (2019) A deep learning model to predict a diagnosis of alzheimer disease by using 18F-FDG PET of the brain. Radiology 290(2):456–464. https://doi.org/10.1148/radiol.2018180958
Wang Z, Chen J, Hoi SCH (2019) Deep learning for image super-resolution: a survey. https://arxiv.org/pdf/1902.06068
van den Oord A, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. https://arxiv.org/pdf/1601.06759
van den Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K (2016) Conditional image generation with pixelCNN decoders. https://arxiv.org/pdf/1606.05328
van den Oord A, Dieleman S, Zen H et al (2016) WaveNet: a generative model for raw audio. https://arxiv.org/pdf/1609.03499
Isola, P, Zhu, J-Y, Zhou T, Efros AA (2016) Image-to-image translation with conditional adversarial networks. https://arxiv.org/pdf/1611.07004
Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in neural information processing systems 27: annual conference on neural information processing systems, pp 2672–2680. https://papers.nips.cc/paper/5423-generative-adversarial-nets
Karras T, Laine S, Aila T (2018) A style-based generator architecture for generative adversarial networks. https://arxiv.org/pdf/1812.04948
Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. http://arxiv.org/pdf/1508.06576v2
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647
Razavi A, van den Oord A, Vinyals O (2019) Generating diverse high-fidelity images with VQ-VAE-2. https://arxiv.org/pdf/1906.00446
Park T, Liu M-Y, Wang T-C, Zhu J-Y (2019) Semantic image synthesis with spatially-adaptive normalization. https://arxiv.org/pdf/1903.07291
Goodfellow I, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. https://arxiv.org/pdf/1412.6572
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Kaufmann, J., Retkowitz, D. (2023). Visualization and Deep Learning in Data Science. In: Barton, T., Müller, C. (eds) Apply Data Science. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-38798-3_2
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DOI: https://doi.org/10.1007/978-3-658-38798-3_2
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