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.
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 within the
context of precision learning.Comment: accepted on ICPR 201