Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification

arXiv:2404.00946v1 Announce Type: new
Abstract: Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increases significantly as batch size shrinks because batch statistics estimates are inaccurate. As a result, computer vision tasks like detection, segmentation, and video, which require tiny batches based on memory consumption, aren't suitable for using Batch Normalization for larger model training and feature transfer. Here, …

abstract alzheimer's arxiv classification cs.cv deep learning disease error multiple networks normalization statistics train type