JavaShuo
栏目
标签
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
时间 2020-12-24
标签
语音识别asr
机器学习
深度学习
栏目
HTML
繁體版
原文
原文链接
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain 1. 论文摘要 依据过去语音片段预测未来片段的CPC方法被证明是一种有效的表征学习方法,本文作者在CPC算法模型的基础上, 通过对过去语音片段在时间域上的数据增强(WavAugment) 取得了比其他方法更高效、更好的表征效果。通过pa
>>阅读原文<<
相关文章
1.
A Simple Framework for Contrastive Learning of Visual Representations
2.
论文写作解读:A Simple Framework for Contrastive Learning of Visual Representations
3.
Article Analysis(AA): A Simple Framework for Contrastive Learning of Visual Representations
4.
Deep Temporal Clustering: Fully unsupervised learning of time-domain features
5.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations学习与理解
6.
Exploring the teaching of deep learning in neural networks
7.
[ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
8.
Contrastive Learning Of Structured World Models
9.
The Rise of Meta Learning
10.
《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》笔记
更多相关文章...
•
PHP time() 函数
-
PHP参考手册
•
SQL IN 操作符
-
SQL 教程
•
JDK13 GA发布:5大特性解读
•
Java Agent入门实战(一)-Instrumentation介绍与使用
相关标签/搜索
speech
contrastive
representations
THE LAST TIME
domain
learning
time
data
DATA+++
for...of
HTML
Spring教程
MyBatis教程
0
分享到微博
分享到微信
分享到QQ
每日一句
每一个你不满意的现在,都有一个你没有努力的曾经。
最新文章
1.
js中 charCodeAt
2.
Android中通过ViewHelper.setTranslationY实现View移动控制(NineOldAndroids开源项目)
3.
【Android】日常记录:BottomNavigationView自定义样式,修改点击后图片
4.
maya 文件检查 ui和数据分离 (一)
5.
eclipse 修改项目的jdk版本
6.
Android InputMethod设置
7.
Simulink中Bus Selector出现很多? ? ?
8.
【Openfire笔记】启动Mac版Openfire时提示“系统偏好设置错误”
9.
AutoPLP在偏好标签中的生产与应用
10.
数据库关闭的四种方式
本站公众号
欢迎关注本站公众号,获取更多信息
相关文章
1.
A Simple Framework for Contrastive Learning of Visual Representations
2.
论文写作解读:A Simple Framework for Contrastive Learning of Visual Representations
3.
Article Analysis(AA): A Simple Framework for Contrastive Learning of Visual Representations
4.
Deep Temporal Clustering: Fully unsupervised learning of time-domain features
5.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations学习与理解
6.
Exploring the teaching of deep learning in neural networks
7.
[ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
8.
Contrastive Learning Of Structured World Models
9.
The Rise of Meta Learning
10.
《A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling》笔记
>>更多相关文章<<