当前位置: X-MOL 学术Comp. Visual Media › 论文详情
Weight asynchronous update: Improving the diversity of filters in a deep convolutional network
Computational Visual Media Pub Date : 2020-10-17 , DOI: 10.1007/s41095-020-0185-5
Dejun Zhang, Linchao He, Mengting Luo, Zhanya Xu, Fazhi He

Deep convolutional networks have obtained remarkable achievements on various visual tasks due to their strong ability to learn a variety of features. A well-trained deep convolutional network can be compressed to 20%–40% of its original size by removing filters that make little contribution, as many overlapping features are generated by redundant filters. Model compression can reduce the number of unnecessary filters but does not take advantage of redundant filters since the training phase is not affected. Modern networks with residual, dense connections and inception blocks are considered to be able to mitigate the overlap in convolutional filters, but do not necessarily overcome the issue. To do so, we propose a new training strategy, weight asynchronous update, which helps to significantly increase the diversity of filters and enhance the representation ability of the network. The proposed method can be widely applied to different convolutional networks without changing the network topology. Our experiments show that the stochastic subset of filters updated in different iterations can significantly reduce filter overlap in convolutional networks. Extensive experiments show that our method yields noteworthy improvements in neural network performance.

更新日期:2020-10-17

 

全部期刊列表>>
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
3分钟学术视频演讲大赛
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
麻省大学
西北大学
湖南大学
华东师范大学
王要兵
化学所
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
陆军军医大学
杨财广
廖矿标
试剂库存
down
wechat
bug