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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王勝德(Sheng-De Wang) | |
| dc.contributor.author | Yen-Cheng Liu | en |
| dc.contributor.author | 劉彥成 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:43:28Z | - |
| dc.date.available | 2017-08-02 | |
| dc.date.copyright | 2017-08-02 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-26 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67674 | - |
| dc.description.abstract | 深度生成模型於電腦視覺與機器學習領域中,近期有顯著發展與影響。特徵解離主要在不可分析之潛在向量中分離出具有語義之個別特徵,傳統方法多為監督學習框架,少數非監督學習框架則無法確保解離語義之穩定性。本篇論文中,我們將提出深度生成類神經網路架構,在單邊領域監督之下,達成跨領域解離語義特徵學習,同時在本文中,我們應用非監督領域適應之概念,學習共同特徵解離與適應。藉由生成對抗學習架構,本文將出新式具特徵解離能力之深度學習架構,此架構將同時訓練於跨領域資料,學習出具有共同語義之分離特徵,進而在生成模型框架之下,完成單領域監督之跨領域深度解離特徵學習。本文實驗中,我們利用此深度生成架構,將原始輸入影像於潛在空間空改變屬性後,生成對應屬性之跨領域影像。同時也將呈現單邊監督情況之下,利用此深度網路架構,完成雙邊領域個別之影像分類,解決非監督領域適應影像分類問題。 | zh_TW |
| dc.description.abstract | The recent progress and development of deep generative models have led to remarkable improvements in research topics in computer vision and machine learning. In this article, the task of cross-domain feature disentanglement is addressed. This thesis advances the idea of unsupervised domain adaptation and propose to perform joint feature disentanglement and adaptation. Based on generative adversarial networks, a novel deep learning architecture with disentanglement ability is presented, which observes cross-domain image data and derives latent features with the underlying factors(e.g., attributes). As a result, our generative model is able to address cross-domain feature disentanglement with only the (attribute) supervision from the source-domain data (not the target-domain ones). In the experiments, the model is applied for generating and classifying images with particular attributes, and show that satisfactory results can be produced. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:43:28Z (GMT). No. of bitstreams: 1 ntu-106-R04921003-1.pdf: 2225062 bytes, checksum: 9466a74e02e531cdf9bc726a44ce0091 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝................................................................................................................................... i
中文摘要.......................................................................................................................... ii ABSTRACT .................................................................................................................... iii CONTENTS .................................................................................................................... iv LIST OF FIGURES......................................................................................................... vi LIST OF TABLES......................................................................................................... viii Chapter 1 Introduction..............................................................................................1 Chapter 2 Preliminaries ............................................................................................5 2.1 Generative Adversarial Networks...................................................................5 2.2 Variational Autoencoder .................................................................................6 2.3 Perceptual Loss...............................................................................................8 Chapter 3 Related Works..........................................................................................9 3.1 Image Synthesis and Image-to-Image Translation .........................................9 3.2 Feature Disentanglement for Image Synthesis.............................................10 3.3 Adaptation Across Visual Domains..............................................................11 Chapter 4 Methodology ...........................................................................................13 4.1 Problem Definition and Notation..................................................................13 4.2 Learning Disentangled Feature Representation in a Single Domain............14 4.3 Learning Cross-Domain Disentangled Representation ................................17 4.4 Objectives of Cross-Domain Disentanglement ............................................20 Chapter 5 Experiment .............................................................................................22 5.1 Implementation.............................................................................................22 5.2 Training Detail..............................................................................................23 5.3 Conditional Image Synthesis and Translation ..............................................25 5.4 Cross-Domain Visual Classification.............................................................27 Chapter 6 Conclusion ..............................................................................................30 REFERENCE ..................................................................................................................31 | |
| dc.language.iso | en | |
| dc.subject | 解離特徵 | zh_TW |
| dc.subject | 對抗生成學習網路 | zh_TW |
| dc.subject | 多樣式自動編碼器 | zh_TW |
| dc.subject | 領域適應 | zh_TW |
| dc.subject | Feature Disentanglement | en |
| dc.subject | Domain Adaptation | en |
| dc.subject | Generative Adversarial Networks | en |
| dc.subject | Variational Autoencoder | en |
| dc.title | 單領域監督之跨領域深度解離特徵學習 | zh_TW |
| dc.title | Learning Cross-Domain Feature Disentanglement with Supervision from A Single Domain | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 王鈺強(Yu-Chiang Frank Wang) | |
| dc.contributor.oralexamcommittee | 李宏毅(Hung-yi Lee),邱維辰(Wei-Chen Chiu) | |
| dc.subject.keyword | 解離特徵,領域適應,對抗生成學習網路,多樣式自動編碼器, | zh_TW |
| dc.subject.keyword | Feature Disentanglement,Domain Adaptation,Generative Adversarial Networks,Variational Autoencoder, | en |
| dc.relation.page | 35 | |
| dc.identifier.doi | 10.6342/NTU201702026 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-27 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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