A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios

Liu, F; Zhang, G; Lu, J

A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios

Liu, F

Zhang, G

Lu, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2019, 2019-June, pp. 1-6
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date	2019-06-23
dc.date.accessioned	2020-06-16T07:59:29Z
dc.date.available	2021-08-12T19:00:44Z
dc.date.issued	2019-06-01
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2019, 2019-June, pp. 1-6
dc.identifier.isbn	9781538617281
dc.identifier.issn	1098-7584
dc.identifier.uri	http://hdl.handle.net/10453/141446
dc.description.abstract	© 2019 IEEE. How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2019.8858889
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios
dc.type	Conference Proceeding
utslib.citation.volume	2019-June
utslib.location.activity	New Orleans, LA, USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-16T07:59:25Z
pubs.finish-date	2019-06-26
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-06-23
pubs.volume	2019-June
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets.

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http://hdl.handle.net/10453/141446