A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups
Institute of Robotics and Machine Intelligence（机器人与机器智能研究所）；Poznan University of Technology（波兹南理工大学）
英文摘要：
We propose a computationally efficient $G$-invariant neural network that approximates functions invariant to the action of a given permutation subgroup $G \leq S_n$ of the symmetric group on input data. The key element of the proposed network architecture is a new $G$-invariant transformation module, which produces a $G$-invariant latent representation of the input data. Theoretical considerations are supported by numerical experiments, which demonstrate the effectiveness and strong generalization properties of the proposed method in comparison to other $G$-invariant neural networks.
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