Learning Continuous Rotation Canonicalization with Radial Beam Sampling. (arXiv:2206.10690v1 [cs.CV])

Web: http://arxiv.org/abs/2206.10690

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 …

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