Abstract
In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space to the output domain. We demonstrate that use of known transforms is able to change maximal error bounds and that these are additive for the entire sequence of transforms. In order to explore the effect further, we consider the problem of X-ray material decomposition as an example to incorporate additional prior knowledge. We demonstrate that inclusion of a non-linear function known from the physical properties of the system is able to reduce prediction errors therewith improving prediction quality from SSIM values of 0.54 to 0.88. This approach is applicable to a wide set of applications in physics and signal processing that provide prior knowledge on such transforms. Also maximal error estimation and network understanding could be facilitated using this novel concept of precision learning.
Original language | English |
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Title of host publication | 2018 24th International Conference on Pattern Recognition, ICPR 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 183-188 |
Number of pages | 6 |
ISBN (Electronic) | 9781538637883 |
DOIs | |
State | Published - 26 Nov 2018 |
Event | 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China Duration: 20 Aug 2018 → 24 Aug 2018 |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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Volume | 2018-August |
ISSN (Print) | 1051-4651 |
Conference
Conference | 24th International Conference on Pattern Recognition, ICPR 2018 |
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Country/Territory | China |
City | Beijing |
Period | 20/08/18 → 24/08/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.