Back to Search Start Over

Multi-Source Deep Transfer Neural Network Algorithm

Authors :
Jingmei Li
Weifei Wu
Di Xue
Peng Gao
Source :
Sensors, Vol 19, Iss 18, p 3992 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.

Details

Language :
English
ISSN :
14248220 and 19183992
Volume :
19
Issue :
18
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.3e688748d4834a0cbc3b8f062a42c057
Document Type :
article
Full Text :
https://doi.org/10.3390/s19183992