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Deep Generative Models for Vision an...
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Duke University.
Deep Generative Models for Vision and Language Intelligence.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Deep Generative Models for Vision and Language Intelligence./
Author:
Gan, Zhe.
Description:
1 online resource (168 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Subject:
Artificial intelligence. -
Online resource:
click for full text (PQDT)
ISBN:
9780355870176
Deep Generative Models for Vision and Language Intelligence.
Gan, Zhe.
Deep Generative Models for Vision and Language Intelligence.
- 1 online resource (168 pages)
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Thesis (Ph.D.)--Duke University, 2018.
Includes bibliographical references
Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I will mainly discuss the contributions that I have made in this field during my Ph.D. study. Specifically, the dissertation is divided into two parts.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355870176Subjects--Topical Terms:
559380
Artificial intelligence.
Index Terms--Genre/Form:
554714
Electronic books.
Deep Generative Models for Vision and Language Intelligence.
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Adviser: Lawrence Carin.
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Includes bibliographical references
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Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I will mainly discuss the contributions that I have made in this field during my Ph.D. study. Specifically, the dissertation is divided into two parts.
520
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In the first part, I will mainly focus on one specific kind of deep directed generative model, called Sigmoid Belief Network (SBN). First, I will present a fully Bayesian algorithm for efficient learning and inference of SBN. Second, since the original SBN can be only used for binary image modeling, I will also discuss the generalization of it to model spare count-valued data for topic modeling, and sequential data for motion capture synthesis, music generation and dynamic topic modeling.
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In the second part, I will mainly focus on visual captioning ( i.e., image-to-text generation), and conditional image synthesis. Specifically, I will first present Semantic Compositional Network for visual captioning, and emphasize interpretability and controllability revealed in the learning algorithm, via a mixture-of-experts design, and the usage of detected semantic concepts. I will then present Triangle Generative Adversarial Network, which is a general framework that can be used for joint distribution matching and learning the bidirectional mappings between two different domains. We consider the joint modeling of image-label, image-image and image-attribute pairs, with applications in semi-supervised image classification, image-to-image translation and attribute-based image editing.
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click for full text (PQDT)
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