Precision Learning

Precision Learning is a research direction, seeking 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.

Projects

Participating Scientists

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Publications

• Huang Y., Taubmann O., Huang X., Haase V., Lauritsch G., Maier A.:
  A New Scale Space Total Variation Algorithm for Limited Angle Tomography
  CT-Meeting 2016 (Bamberg, Germany, April 13, 2016 - April 16, 2016)
  In: Marc Kachelrieß (ed.): CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography) 2016
  URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Huang16-ANS.pdf
  BibTeX: Download
• Huang Y., Taubmann O., Huang X., Lauritsch G., Maier A.:
  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
  BibTeX: Download
• Haase V., Taubmann O., Huang Y., Krings G., Lauritsch G., Maier A., Mertins A.:
  Make the Most of Time: Temporal Extension of the iTV Algorithm for 4D Cardiac C-Arm CT
  Bildverarbeitung für die Medizin 2016 (Berlin)
  In: Bildverarbeitung für die Medizin 2016, Berlin Heidelberg: 2016
  DOI: 10.1007/978-3-662-49465-3_31
  URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Haase16-MTM.pdf
  BibTeX: Download