Semi-supervised Generative Adversarial Networks Based on Scalable Support Vector Machines and Manifold Regularization
作者单位：College of AutomationChongqing University of Posts and Telecommunications College of Computer Science and TechnologyChongqing University of Posts and Telecommunications College of Union with University of CincinnatiUniversity of Chongqing
会议名称：《第32届中国控制与决策会议》
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Generative adversarial networks semi-supervised learning Scalable SVM Manifold Regularization
摘 要：Generative adversarial networks（GANs） are potential models in semi-supervised learning because of the excellent performance of ***,most GAN-based semi-supervised models are sensitive to local perturbation,which means those models are not stable ***,Softmax classifier is the first choice of those *** this paper,a novel method is proposed by introducing a discriminator using scalable SVM classifier with manifold *** SVM classifiers typically perform better in small sample data sets compared with other classifiers,which is consistent with the feature that semi-supervised learning consists of a few labeled data and a large number of unlabeled *** regularization forces discriminator to keep invariable to local *** incorporating into feature-matching GAN architecture,the proposed GANs-based semi-supervised learning algorithm has advantages over other methods on the Cifar-10,SVHN and Cifar-100 *** results show that the proposed model SSVM-GAN has good robustness and strong generalization ability.