Transfer Learning with Joint Inter-class and Inter-domain Distributional Adaptation
LI Ping1,2,3, NI Zhiwei1,3, ZHU Xuhui1,3 , SONG Juan1,3
1. School of Management, Hefei University of Technology, Hefei 230009; 2. College of Information Engineering, Fuyang Normal University, Fuyang 236041; 3. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009
Abstract Inter-domain information is lost during the process of inter-domain distributional adaptation. Therefore, it is difficult to train an effective classifier in the source domain, and the performance of generalization and tagging in the target domain are affected. Aiming at this problem, an approach, joint inter-class and inter-domain distributional adaptation for transfer learning, is proposed to address this challenge. The proposed method is formulated by learning a projection matrix to map new representations of respective domains into a common subspace. And the distance-measure method of the maximum mean discrepancy is adopted to compute the distance of inter-class and inter-domain distributions. During the optimization procedure, the inter-domain distributional difference is reduced explicitly, and the inter-class distributional difference is enlarged greatly. The capability of knowledge transfer between different domains is improved. Experiments on transfer learning dataset verify the effectiveness of the proposed approach.
Fund:Supported by National Natural Science Foundation of China(No.91546108,71490725,71521001), Natural Science Foundation of Anhui Province(No.1708085MG169), Key Program of Natural Science of Higher Education Institutions of Anhui Province(No.KJ2019A0949), Teaching Research Program of Higher Education Institutions of Anhui Province(No.2018jyxm0264)
Corresponding Authors:
NI Zhiwei, Ph.D., professor. His research interests include artificial intelligence, machine learning and cloud computing.
About author:: LI Ping, Ph.D. candidate, lecturer. Her research interests include pattern recognition and machine learning.ZHU Xuhui, Ph.D., lecturer. His research interests include intelligent computing and machine learning.SONG Juan, Ph.D. candidate. Her research interests include intelligent computing and machine learning.
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2020, Vol. 33 
