Meta-Learning Symmetries by Reparameterization
This addresses the need for custom task-specific architectures in deep learning, offering a more flexible approach for practitioners.
The paper tackles the problem of manually designing equivariant architectures by introducing a method to learn symmetries and encode equivariances into networks from data, achieving automatic learning of common transformations in image processing tasks.
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.