Small sample learning with high order contractive auto-encoders and application in SAR images  

作　　者：Qianwen YANG Fuchun SUN 

机构地区：[1]Department of Computer Science and Technology, Tsinghua University [2]State Key Lab of Intelligent Technology and Systems, Tsinghua University [3]Tsinghua National Laboratory for Information Science and Technology, Tsinghua University

出　　处：《Science China(Information Sciences)》2018年第9期271-273,共3页中国科学（信息科学）（英文版）

摘　　要：Dear editor,Recently auto-encoders（AEs）are used as intermediate layers or unsupervised learning stages in deep learning networks[1].However,unlike other deep learning algorithms,which can extract higher-order abstract features using deep structures [2, 3].Dear editor,Recently auto-encoders（AEs）are used as intermediate layers or unsupervised learning stages in deep learning networks[1].However,unlike other deep learning algorithms,which can extract higher-order abstract features using deep structures [2, 3].

关 键 词：SAR Small sample learning with high order contractive auto-encoders and application in SAR images MSTAR RBM 

分 类 号：TN957.52[电子电信—信号与信息处理]