  Simple image rotations significantly reduce the accuracy of deep neural
networks. Moreover, training with all possible rotations increases the data
set, which also increases the training duration. In this work, we address
trainable rotation invariant convolutions as well as the construction of nets,
since fully connected layers can only be rotation invariant with a
one-dimensional input. On the one hand, we show that our approach is
rotationally invariant for different models and on different public data sets.
We also discuss the influence of purely rotational invariant features on
accuracy. The rotationally adaptive convolution models presented in this work
are more computationally intensive than normal convolution models. Therefore,
we also present a depth wise separable approach with radial convolution. Link
to CUDA code
https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/
