Conceptual Domain Adaptation using Deep Learning

Simple item page
dc.contributor.advisorVilalta, Ricardo
dc.contributor.committeeMemberCheng, Albert M. K.
dc.contributor.committeeMemberOrdonez, Carlos
dc.contributor.committeeMemberKaiser, Klaus
dc.creatorMehrparvar, Behrang 1984-
dc.date.accessioned2019-09-18T02:08:20Z
dc.date.available2019-09-18T02:08:20Z
dc.date.createdAugust 2017
dc.date.issued2017-08
dc.date.submittedAugust 2017
dc.date.updated2019-09-18T02:08:21Z
dc.description.abstractDomain adaptation scenarios have been successively gaining attention in practical applications of machine learning. Here, the source distribution in which the classifier is trained, differs from the target distribution in which the classifier will ultimately be applied. The discrepancy between source and target distributions often results in poor predictive performance. Recent work shows several approaches providing a solution to alleviate the problem of distribution discrepancy. Deep neural networks extract high-level abstract representations of data, and have been used to transform the source and target data into a new common space, such that the aforementioned discrepancy is minimized. In this document, we introduce conceptual domain adaptation where semantic information hidden in high-level concepts -- as opposed to information from low-level representational properties of the data -- is directly used for adaptation. We investigate current deep learning-based domain adaptation approaches and argue that due to their reliance on representational properties of data, using them for domain adaptation is prone to failure under certain scenarios. These scenarios are investigated as cases with inherently lower-level representational discrepancy. In this project, we introduce an adjustment approach as a solution towards conceptual domain adaptation. Accordingly, we propose a search framework to adjust high-level representation of target data along with basic supervised, graph-based and PCA-based fitness evaluation. Based on experimental results, we contend that the proposed solution is beneficial for domain adaptation problems with lower-level representational in supervised scenarios.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/4808
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectDomain adaptation
dc.subjectDeep learning
dc.titleConceptual Domain Adaptation using Deep Learning
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
MEHRPARVAR-DISSERTATION-2017.pdf
Size:
621 KB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.43 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.82 KB
Format:
Plain Text
Description:
Download

Collections

Published ETD Collection