Machine learning
批量归一化
批量归一化（Batch Normalization）是Sergey Ioffe和Christian Szegedy于2015年提出的一种训练技术，它利用当前小批量（mini-batch）计算出的均值和方差来归一化每一层的前激活输出。通过稳定训练过程中每一层输入的分布，它能显著减少内部协变量偏移（internal covariate shift），从而允许使用更高的学习率，并使深度网络训练得更快、更可靠。
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来源
- Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 448–456. link ↗
- Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning (Ch. 8). MIT Press. ISBN: 978-0-262-03561-3
- Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv preprint arXiv:1502.03167. link ↗
如何引用本页
ScholarGate. (2026, June 3). Batch Normalization (Normalizing Layer Activations per Mini-Batch). ScholarGate. https://scholargate.app/zh/deep-learning/batch-normalization
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