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

Rotated Ring, Radial and Depth Wise Separable Radial Convolutions