JavaShuo
栏目
标签
A3M:Attribute-Aware Attention Model for Fine-grained Representation Learning
时间 2021-01-11
原文
原文链接
code 一条支路直接做ID分类(Category),其余分支做属性分类。 两个attention模块,一个是 即属性在Category Feature Map中找到属性对应的部分(attention) 另一个是: Category特征嵌入到属性特征中去找哪些属性特征利于Category分类(Attention)。 最终达到的效果是ap更近,an更远: 效果(并不高):
>>阅读原文<<
相关文章
1.
《VideoBERT: A Joint Model for Video and Language Representation Learning》
2.
An Unsupervised Autoregressive Model for Speech Representation Learning
3.
[AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
4.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
5.
recurrent model for visual attention
6.
Hierarchical Attention Based Semi-supervised Network Representation Learning
7.
Predictive learning vs. representation learning
8.
Self-Supervised Representation Learning
9.
Building Program Vector Representation for Deep Learning
10.
Momentum Contrast for Unsupervised Visual Representation Learning
更多相关文章...
•
Swift for 循环
-
Swift 教程
•
Scala for循环
-
Scala教程
•
Java Agent入门实战(三)-JVM Attach原理与使用
•
Java Agent入门实战(一)-Instrumentation介绍与使用
相关标签/搜索
representation
attention
learning
model
bilstm+attention
Deep Learning
Meta-learning
Learning Perl
model&animation
for...of
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.
python的安装和Hello,World编写
2.
重磅解读:K8s Cluster Autoscaler模块及对应华为云插件Deep Dive
3.
鸿蒙学习笔记2(永不断更)
4.
static关键字 和构造代码块
5.
JVM笔记
6.
无法启动 C/C++ 语言服务器。IntelliSense 功能将被禁用。错误: Missing binary at c:\Users\MSI-NB\.vscode\extensions\ms-vsc
7.
【Hive】Hive返回码状态含义
8.
Java树形结构递归(以时间换空间)和非递归(以空间换时间)
9.
数据预处理---缺失值
10.
都要2021年了,现代C++有什么值得我们学习的?
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.
《VideoBERT: A Joint Model for Video and Language Representation Learning》
2.
An Unsupervised Autoregressive Model for Speech Representation Learning
3.
[AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
4.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
5.
recurrent model for visual attention
6.
Hierarchical Attention Based Semi-supervised Network Representation Learning
7.
Predictive learning vs. representation learning
8.
Self-Supervised Representation Learning
9.
Building Program Vector Representation for Deep Learning
10.
Momentum Contrast for Unsupervised Visual Representation Learning
>>更多相关文章<<