LieBN: Batch Normalization over Lie Groups
Recent advances have extended Deep Neural Networks (DNNs) to operate on manifolds, accompanied by normalization techniques tailored to different geometries, collectively referred to as Riemannian normalization. However, most existing Riemannian normalization methods are either designed for specific manifolds or fail to effectively normalize manifold-valued sample distributions. To address these limitations, we propose LieBN, a framework for Riemannian Batch Normalization (RBN) over Lie groups.
- ▪Recent advances have extended Deep Neural Networks (DNNs) to operate on manifolds, accompanied by normalization techniques tailored to different geometries, collectively referred to as Riemannian normalization.
- ▪However, most existing Riemannian normalization methods are either designed for specific manifolds or fail to effectively normalize manifold-valued sample distributions.
- ▪To address these limitations, we propose LieBN, a framework for Riemannian Batch Normalization (RBN) over Lie groups.
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Computer Science > Machine Learning arXiv:2607.08783 (cs) [Submitted on 13 Jun 2026] Title:LieBN: Batch Normalization over Lie Groups Authors:Ziheng Chen, Yue Song, Rui Wang, Xiao-Jun Wu, Nicu Sebe View a PDF of the paper titled LieBN: Batch Normalization over Lie Groups, by Ziheng Chen and 4 other authors View PDF HTML (experimental) Abstract:Manifold-valued measurements are prevalent in various machine learning tasks. Recent advances have extended Deep Neural Networks (DNNs) to operate on manifolds, accompanied by normalization techniques tailored to different geometries, collectively referred to as Riemannian normalization.
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