Batch Normalization

Batch Normalization, a technique introduced to stabilize deep neural network training, has become a cornerstone of modern deep learning, particularly in convolutional neural networks for computer visi…

Deep neural networks face training challenges like vanishing gradients and slow convergence. Techniques such as activation functions, Xavier and He weight initialization, and Batch Normalization help …

Researchers at arXiv have identified that Batch Normalization causes gradient skew in dynamic sparse training (DST) methods, leading to slower convergence compared to dense neural network training. Th…