.png){kind=icon}
返回
Non-Negative Transfer Learning With Consistent Inter-Domain Distribution
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2020.3025061.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we propose a novel transfer learning approach, which simultaneously exploits the intra-domain differentiation and inter-domain correlation to comprehensively solve the drawbacks many existing transfer learning methods suffer from, i.e., they either are unable to handle the negative samples or have strict assumptions on the distribution. Specifically, the sample selection strategy is introduced to handle negative samples by using the local geometry structure and the label information of source samples. Furthermore, the pseudo target label is imposed to slack the assumption on the inter-domain distribution for considering the inter-domain correlation. Then, an efficient alternating iterative algorithm is proposed to solve the formulated optimization problem with multiple constraints. The extensive experiments conducted on eleven real-world datasets show the superiority of our method over state-of-the-art approaches, i.e., our method achieves 11.23% improvement on the MNIST dataset.
Keyword:
Correlation
Optimization
Knowledge transfer
Task analysis
Learning systems
Signal processing algorithms
Kernel
Transfer learning
inter-domain distribution
negative transfer
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
DrosophilaNeurexin IV Interacts with Roundabout and Is Required for Repulsive Midline Axon Guidance
OAAI
OAAI分享收藏分享收藏
查看更多内容