Investigating Learning in Deep Neural Networks using Layer-Wise Weight Change

Autor:	Agrawal, Ayush Manish, Tendle, Atharva, Sikka, Harshvardhan, Singh, Sahib, Kayid, Amr
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages, 20 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.06735 Zobrazit plný text záznamu View this record from Arxiv