Precision Learning
Precision Learning is a research direction, seeking
to integrate known operators into machine learning models to improve
generalization und efficiency.
to integrate known operators into machine learning models to improve
generalization und efficiency.
Known operators have been shown to hold the
potential of reducing maximal error bounds when incorporated into deep
neural networks. This suggests their inclusion could allow models to
learn from less data and increase robustness.
Colloqium timetable
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Projects
Publications
Papoulis-Gerchberg Algorithms for Limited Angle Tomography Using Data Consistency Conditions
the 5th International Conference on Image Formation in X-ray Computed Tomography (Salt Lake City, Utah, the USA, May 20, 2018 - May 23, 2018)
In: Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, Salt Lake City, Utah, the USA: 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Huang18-PAF.pdf
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Robust mixed one-bit compressive sensing
In: Signal Processing 162 (2019), p. 161-168
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2019.04.011
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Restoration of Missing Data in Limited Angle Tomography Based on Consistency Conditions
The 14th International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Xi'an, China, June 18, 2017 - June 23, 2017)
In: Ge Wang and Xuanqin Mou (ed.): The 14th International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine 2017
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Huang17-ROM.pdf
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Traditional machine learning for limited angle tomography
In: International Journal of Computer Assisted Radiology and Surgery 8/2018 (2018), p. 1-9
ISSN: 1861-6410
DOI: 10.1007/s11548-018-1851-2
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Huang18-TML_IJCARS.pdf
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Scale-Space Anisotropic Total Variation for Limited Angle Tomography
In: IEEE Transactions on Radiation and Plasma Medical Sciences 2 (2018), p. 307-314
ISSN: 2469-7311
DOI: 10.1109/TRPMS.2018.2824400
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Huang18-SAT.pdf
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Persons
Leonid Mill, M. Sc.
- Phone number: +49 9131 85-25247
- Email: leonid.mill@fau.de
- Website: https://lme.tf.fau.de/person/mill/
Dr.-Ing. Yixing Huang
- Phone number: +49 9131 85-25247
- Email: yixing.yh.huang@fau.de
- Website: https://lme.tf.fau.de/person/huang