IEEE Access | |
SiCoDeF² Net: Siamese Convolution Deconvolution Feature Fusion Network for One-Shot Classification | |
Antonio Robles-Gomez1  Rafael Pastor-Vargas1  Mercedes E. Paoletti2  Purbayan Kar3  Swalpa Kumar Roy3  Juan M. Haut4  | |
[1] Department of Communication and Control Systems, Higher School of Computer Engineering, National Distance Education University, Madrid, Spain;Department of Computer Architecture, Faculty of Computer Science, Complutense University, Madrid, Spain;Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India;Department of Computers and Communications, Escuela Politecnica, University of Extremadura, Caceres, Spain; | |
关键词: Convolutional neural networks (CNNs); deep learning; face recognition; one-shot learning; | |
DOI : 10.1109/ACCESS.2021.3107626 | |
来源: DOAJ |
【 摘 要 】
Nowadays, deep convolutional neural networks (CNNs) for face recognition exhibit a performance comparable to human ability in the presence of the appropriate amount of labelled training data. However, training CNNs remains as an arduous task due to the lack of training samples. To overcome this drawback, applications demand one-shot learning to improve the obtained performances over traditional machine learning approaches by learning representative information about data categories from few training samples. In this context, Siamese convolutional network (
【 授权许可】
Unknown