计算机与数字工程2019，Vol.47Issue(10)：2428-2433,6.DOI:10.3969/j.issn.1672-9722.2019.10.010
基于语义自动编码机的零次学习研究∗
Research on Semantic Auto-encoder for Zero-Shot Learning
王阳 1王琼 1陆建峰1
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京 210094
- 折叠
摘要
Abstract
Zero-Shot Learning(ZSL)is one of the most important research fields for computer science with broad application prospects and potentials. Zero-Shot Learning algorithms recognize or classify test samples through features extracted from training samples,and any test samples are forbidden during the training phrase. Auto-encoder projects the original features into certain code space and inverse operation is offered simultaneously which makes this structure reserving the distribution of the original feature space. By adding some constraints,it makes auto-encoder compatible with semantic features. The answer can be obtained by solv?ing a Sylvester equation,with regularization or kernel trick for developing its behavior. Experimental results achieve the leading lev?el in present.关键词
零次学习/自动编码机/语义特征/神经网络/核函数Key words
Zero-Shot Learning/auto-encoder/semantic/neural network/kernel分类
信息技术与安全科学引用本文复制引用
王阳,王琼,陆建峰..基于语义自动编码机的零次学习研究∗[J].计算机与数字工程,2019,47(10):2428-2433,6.
