论文标题
使用层的重量变化研究深度神经网络中的学习
Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change
论文作者
论文摘要
了解深神经网络的每层学习动态具有重大兴趣，因为它可以提供有关神经网络学习方式和更好训练方案的潜力的见解。我们通过在训练时测量层的相对重量变化来研究深卷卷神经网络（CNN）中的学习。各种计算机视觉分类任务的各种CNN体系结构中出现了几种有趣的趋势，包括与早期相比，以后层的相对重量变化的总体增加。
Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for better training regimens. We investigate learning in Deep Convolutional Neural Networks (CNNs) by measuring the relative weight change of layers while training. Several interesting trends emerge in a variety of CNN architectures across various computer vision classification tasks, including the overall increase in relative weight change of later layers as compared to earlier ones.
