Steerable CNN imposes the prior knowledge of transformation invariance or
equivariance in the network architecture to enhance the the network robustness
on geometry transformation of data and reduce overfitting. It has been an
intuitive and widely used technique to construct a steerable filter by
augmenting a filter with its transformed copies in the past decades, which is
named as filter transform in this paper. Recently, the problem of steerable CNN
has been studied from aspect of group representation theory, which reveals the
function space structure of a steerable kernel function. However, it is not yet
clear on how this theory is related to the filter transform technique. In this
paper, we show that kernel constructed by filter transform can also be
interpreted in the group representation theory. This interpretation help
complete the puzzle of steerable CNN theory and provides a novel and simple
approach to implement steerable convolution operators. Experiments are executed
on multiple datasets to verify the feasibility of the proposed approach.

本文介绍一种名为 “可转向 CNN” 的神经网络结构，应用于数据的几何变换，通过滤波器变换技术构建可转向卷积核，通过群表示理论将此两者联系在一起，提高了网络的鲁棒性并减少了过拟合。