Clebsch-Gordan网：一种全傅立叶空间球面卷积神经网络
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
摘要：
Cohen \emph{et al.}最近的工作通过使用群表示理论和非交换调和分析的思想，以旋转不变的方式学习球面图像，取得了最先进的结果。在本文中，我们提出了一个概括的这项工作，一般表现出更好的性能，但从实现的角度来看，实际上是更简单。所提出的架构的一个不寻常的功能是，它使用的Clebsch-Gordan变换作为其唯一的非线性源，从而避免重复的前向和后向傅立叶变换。本文的基本思想推广到构造神经网络是不变的其他紧凑组的行动。
英文摘要：
Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
