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Learning disentangled representations in deep visual analysis
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Peng, Xi. Learning disentangled representations in deep visual analysis. Retrieved from https://doi.org/doi:10.7282/T30868JM

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TitleLearning disentangled representations in deep visual analysis
NamePeng, Xi (author); Metaxas, Dimitris N (chair); Michmizos, Konstantinos (internal member); Yu, Jingjin (internal member); Liu, Xiaoming (outside member); Rutgers University; School of Graduate Studies
Date Created2018
Other Date2018-01 (degree)
SubjectComputer Science, Machine learning
Extent1 online resource (xvii, 113 p. : ill.)
DescriptionLearning reliable and interpretable representations is one of the fundamental challenges in machine learning and computer vision. Over the last decade, deep neural networks have achieved remarkable success by learning conditional distributions on the data for the purposes of solving different tasks. However, representations learned by deep models do not always manifest consistent meaning along variations: many latent factors are highly entangled. As a result, tremendous data annotations and sophisticated training skills are required, even though flawed representations with undesirable characteristics are still produced from time to time. In this work, we are interested in learning disentangled representations that encode distinct aspects of the data separately. The objective is to decouple the latent factors in a representation space, where factorizable structures are obtained and consistent semantics are associated with different variables. The disentanglement can be learned in an either supervised or self-supervised manner. Especially, we investigate three different visual analysis tasks: viewpoint estimation, landmark localization, and large-pose recognition. We show that, by learning disentangled representations, deep models are efficient to train and robust to variations, achieving state-of-the-art performance in challenging conditions.
NotePh.D.
NoteIncludes bibliographical references
Noteby Xi Peng
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T30868JM
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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