  Neural networks are widely used for almost any task of recognizing image
content. Even though much effort has been put into investigating efficient
network architectures, optimizers, and training strategies, the influence of
image interpolation on the performance of neural networks is not well studied.
Furthermore, research has shown that neural networks are often sensitive to
minor changes in the input image leading to drastic drops of their performance.
Therefore, we propose the use of keypoint agnostic frequency selective
mesh-to-grid resampling (FSMR) for the processing of input data for neural
networks in this paper. This model-based interpolation method already showed
that it is capable of outperforming common interpolation methods in terms of
PSNR. Using an extensive experimental evaluation we show that depending on the
network architecture and classification task the application of FSMR during
training aids the learning process. Furthermore, we show that the usage of FSMR
in the application phase is beneficial. The classification accuracy can be
increased by up to 4.31 percentage points for ResNet50 and the Oxflower17
dataset.
