  Many successful deep learning architectures are equivariant to certain
transformations in order to conserve parameters and improve generalization:
most famously, convolution layers are equivariant to shifts of the input. This
approach only works when practitioners know the symmetries of the task and can
manually construct an architecture with the corresponding equivariances. Our
goal is an approach for learning equivariances from data, without needing to
design custom task-specific architectures. We present a method for learning and
encoding equivariances into networks by learning corresponding parameter
sharing patterns from data. Our method can provably represent
equivariance-inducing parameter sharing for any finite group of symmetry
transformations. Our experiments suggest that it can automatically learn to
encode equivariances to common transformations used in image processing tasks.
We provide our experiment code at
https://github.com/AllanYangZhou/metalearning-symmetries.
