Abstract
Transfer learning has been widely applied in Artificial Intelligence of Things (AIoT) to support intelligent services. Typically, collection and collaboration are two mainstreaming methods to improve transfer learning performance, whose efficiency has been evaluated by real-data experimental results but lacks validation of theoretical analysis. In order to provide guidance of implementing transfer learning in real applications, a theoretical analysis is in desired need to help us fully understand how to efficiently improve transfer learning performance. To this end, in this paper, we conduct comprehensive analysis on the methods of enhancing transfer learning performance. More specifically, we prove the answers to three critical questions for transfer learning: i) by comparing collecting instances and collecting attributes, which collection approach is more efficient? ii) is collaborative transfer learning efficient? and iii) by comparing collection with collaboration, which one is more efficient? Our answers and findings can work as fundamental guidance for developing transfer learning.
Honghui Xu 许宏辉
Ph.D. Candidate
My research interests include machine learning, deep learning, and data privacy.