Research on Semantic Auto-encoder for Zero-Shot Learning
WANG Yang
WANG Qiong
LU Jianfeng
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.
Keywords：Zero-Shot Learningauto-encodersemanticneural networkkernel
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2428-2433 )