This content originally appeared on HackerNoon and was authored by Batching
Table of Links
3. Revisiting Normalization
3.1 Revisiting Euclidean Normalization
4 Riemannian Normalization on Lie Groups
5 LieBN on the Lie Groups of SPD Manifolds and 5.1 Deformed Lie Groups of SPD Manifolds
7 Conclusions, Acknowledgments, and References
\ APPENDIX CONTENTS
B Basic layes in SPDnet and TSMNet
C Statistical Results of Scaling in the LieBN
D LieBN as a Natural Generalization of Euclidean BN
E Domain-specific Momentum LieBN for EEG Classification
F Backpropagation of Matrix Functions
G Additional Details and Experiments of LieBN on SPD manifolds
H Preliminary Experiments on Rotation Matrices
I Proofs of the Lemmas and Theories in the Main Paper
3.1 REVISITING EUCLIDEAN NORMALIZATION
In Euclidean DNNs, normalization stands as a pivotal technique for accelerating network training by mitigating the issue of internal covariate shift (Ioffe & Szegedy, 2015). While various normalization methods have been introduced (Ioffe & Szegedy, 2015; Ba et al., 2016; Ulyanov et al., 2016; Wu & He, 2018), they all share a common fundamental concept: the regulation of the first and second statistical moments. In this paper, we focus on batch normalization only.
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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:::info Authors:
(1) Ziheng Chen, University of Trento;
(2) Yue Song, University of Trento and a Corresponding author;
(3) Yunmei Liu, University of Louisville;
(4) Nicu Sebe, University of Trento.
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This content originally appeared on HackerNoon and was authored by Batching
Batching | Sciencx (2025-02-25T03:00:52+00:00) Revisiting Euclidean Normalization: A Second Look. Retrieved from https://www.scien.cx/2025/02/25/revisiting-euclidean-normalization-a-second-look/
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