The principle of equivariance to symmetry transformations enables a
theoretically grounded approach to neural network architecture design.
Equivariant networks have shown excellent performance and data efficiency on
vision and medical imaging problems that exhibit symmetries. Here we show how
this principle can be extended beyond global symmetries to local gauge
transformations. This enables the development of a very general class of
convolutional neural networks on manifolds that depend only on the intrinsic
geometry, and which includes many popular methods from equivariant and
geometric deep learning. We implement gauge equivariant CNNs for signals
defined on the surface of the icosahedron, which provides a reasonable
approximation of the sphere. By choosing to work with this very regular
manifold, we are able to implement the gauge equivariant convolution using a
single conv2d call, making it a highly scalable and practical alternative to
Spherical CNNs. Using this method, we demonstrate substantial improvements over
previous methods on the task of segmenting omnidirectional images and global
climate patterns.

本研究提出了一种基于变换对称性等效性原则的神经网络架构设计方法，并将此原则扩展至局部规范变换领域，以实现在流形上的卷积神经网络，提高了全景图像与全球气候图案分割等任务的性能。