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Riemannian approach to batch normalization
lib:57bb4973848e08a8 (v1.0.0)
Authors: Minhyung Cho,Jaehyung Lee
Where published: NeurIPS 2017 12
ArXiv: 1709.09603
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1709.09603v3
Batch Normalization (BN) has proven to be an effective algorithm for deep neural network training by normalizing the input to each neuron and reducing the internal covariate shift. The space of weight vectors in the BN layer can be naturally interpreted as a Riemannian manifold, which is invariant to linear scaling of weights. Following the intrinsic geometry of this manifold provides a new learning rule that is more efficient and easier to analyze. We also propose intuitive and effective gradient clipping and regularization methods for the proposed algorithm by utilizing the geometry of the manifold. The resulting algorithm consistently outperforms the original BN on various types of network architectures and datasets.
Where published: NeurIPS 2017 12
ArXiv: 1709.09603
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1709.09603v3
Batch Normalization (BN) has proven to be an effective algorithm for deep neural network training by normalizing the input to each neuron and reducing the internal covariate shift. The space of weight vectors in the BN layer can be naturally interpreted as a Riemannian manifold, which is invariant to linear scaling of weights. Following the intrinsic geometry of this manifold provides a new learning rule that is more efficient and easier to analyze. We also propose intuitive and effective gradient clipping and regularization methods for the proposed algorithm by utilizing the geometry of the manifold. The resulting algorithm consistently outperforms the original BN on various types of network architectures and datasets.
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