Deep Neural Networks for Multi-Source Transfer Learning

Transfer learning is gaining incredible attention due to its ability to leverage previously acquired knowledge from source domain to assist in completing a task in a similar target domain. Many existing transfer learning methods deal with single source transfer learning, but rarely consider the fact that information from a single source can be inadequate to a target. In addition, most transfer learning methods assume that the source and target domains share the same label space. But in practice, the source domain(s) sharing the same label space with the target domain may never be found. Third, data privacy and security are being magnificently conspicuous in real-world applications, which means the traditional transfer learning relying on data matching cannot be applied due to privacy concerns. To solve the mentioned problems, this thesis develops a series of methods to tackle transfer learning with multiple source domains. To measure contributions of source domains, multi-source contribution learning and dynamic classifier alignment methods are developed. To define what to transfer, sample and source distillation method is proposed. To address transfer learning without the access to source data, generally auxiliary model and fuzzy rule-based model are explored under closed-set, partial and open-set settings. Finally, universal domain adaptation is exploited by designing a model which is flexible enough to multiple source domains with homogeneous and heterogeneous label spaces.

Author dc:creator

• Li, Keqiuyin

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• © 2022 Elle Keqiuyin Li
• au.edu.uts.lib/nph

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10453/170509

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/170509