國立虎尾科技大學 |

Deep Generative Models for Vision and Language Intelligence.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep Generative Models for Vision and Language Intelligence./
作者:	Gan, Zhe.
面頁冊數:	1 online resource (168 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
標題:	Artificial intelligence. -
電子資源:	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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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Lawrence Carin.
502 $a Thesis (Ph.D.)--Duke University, 2018.
504 $a Includes bibliographical references
520 $a 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 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a Duke University. $b Electrical and Computer Engineering. $3 845695
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