为复数域神经网络设计新型批归一化方法，提升训练稳定性和准确率。
Batch Normalization for Neural Networks on Complex Domains
- 基于黎曼几何设计复数域专用批归一化层
- 在雷达杂波分类等任务上显著提升性能
- 适用于复数域机器学习新场景，如信号处理
黎曼神经网络在多种机器学习任务中表现优异，其成功关键在于构建深度神经网络核心组件的黎曼类比。其中，黎曼批归一化（BN）层已被证明能增强训练稳定性并提高精度。本文提出适用于复数域神经网络的批归一化层，与现有黎曼BN层具有紧密联系。我们推导了在较少研究的复数域（如塞格尔盘域）中实现BN层的关键组件，并在雷达杂波分类、节点分类和动作识别任务上进行了实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of principled Riemannian analogs of fundamental building blocks in deep neural networks (DNNs). Among those, Riemannian batch normalization (BN) layers have shown to enhance training stability and improve accuracy. In this paper, we propose BN layers for neural networks on complex domains. The proposed layers have close connections with existing Riemannian BN layers. We derive essential components for practical implementations of BN layers on some complex domains which are less studied in previous works, e.g., the Siegel disk domain. We conduct experiments on radar clutter classification, node classification, and action recognition demonstrating the efficacy of our method.
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