  Nearly all state of the art vision models are sensitive to image rotations.
Existing methods often compensate for missing inductive biases by using
augmented training data to learn pseudo-invariances. Alongside the resource
demanding data inflation process, predictions often poorly generalize. The
inductive biases inherent to convolutional neural networks allow for
translation equivariance through kernels acting parallely to the horizontal and
vertical axes of the pixel grid. This inductive bias, however, does not allow
for rotation equivariance. We propose a radial beam sampling strategy along
with radial kernels operating on these beams to inherently incorporate
center-rotation covariance. Together with an angle distance loss, we present a
radial beam-based image canonicalization model, short BIC. Our model allows for
maximal continuous angle regression and canonicalizes arbitrary center-rotated
input images. As a pre-processing model, this enables rotation-invariant vision
pipelines with model-agnostic rotation-sensitive downstream predictions. We
show that our end-to-end trained angle regressor is able to predict continuous
rotation angles on several vision datasets, i.e. FashionMNIST, CIFAR10,
COIL100, and LFW.
