Synthetic Data for Visual Machine Learning : : A Data-Centric Approach.
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Superior document: | Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2202 |
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Place / Publishing House: | Linköping : : Linkopings Universitet,, 2022. {copy}2022. |
Year of Publication: | 2022 |
Edition: | 1st ed. |
Language: | English |
Series: | Linköping Studies in Science and Technology. Licentiate Thesis Series
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Physical Description: | 1 online resource (144 pages) |
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Tsirikoglou, Apostolia. Synthetic Data for Visual Machine Learning : A Data-Centric Approach. 1st ed. Linköping : Linkopings Universitet, 2022. {copy}2022. 1 online resource (144 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2202 Intro -- Abstract -- Populärvetenskaplig Sammanfattning -- Acknowledgments -- List of Publications -- Contributions -- Contents -- 1 Introduction -- 1.1 Visual data -- 1.2 Image synthesis -- 1.2.1 Computer graphics -- 1.2.2 Generative image modeling -- 1.3 Deep learning -- 1.3.1 Training data -- 1.4 Objectives -- 1.5 Outline -- 2 Background -- 2.1 Deep learning -- 2.1.1 Neural networks -- 2.1.2 Basic concepts -- 2.1.3 Applications -- 2.2 Computer vision -- 2.3 Digital pathology -- 3 Computer graphics -- 3.1 Modeling -- 3.1.1 Basics -- 3.1.2 Common practices -- 3.1.3 Procedural modeling -- 3.2 Rendering -- 3.2.1 Light transport theory -- 3.2.2 Light transport simulation -- 4 Generative modeling -- 4.1 Fundamentals -- 4.2 Deep generative models -- 4.3 Generative adversarial networks -- 4.3.1 Challenges -- 4.3.2 Common variants -- 5 Synthetic data for deep learning -- 5.1 Data-centric AI -- 5.1.1 Common practices -- 5.2 Data collection -- 5.2.1 Discussion -- 5.3 Data generation -- 5.3.1 Computer graphics -- 5.3.2 Generative adversarial networks -- 5.3.3 Contributions -- 5.3.4 Discussion -- 5.4 Data augmentation -- 5.4.1 Image manipulations -- 5.4.2 Deep learning approaches -- 5.4.3 Contributions -- 5.4.4 Discussion -- 6 Conclusion -- 6.1 Contributions -- 6.1.1 Data generation -- 6.1.2 Data augmentation -- 6.2 Discussion -- 7 Outlook -- Bibliography -- Papers. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Print version: Tsirikoglou, Apostolia Synthetic Data for Visual Machine Learning Linköping : Linkopings Universitet,c2022 9789179291747 ProQuest (Firm) Linköping Studies in Science and Technology. Licentiate Thesis Series https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30229654 Click to View |
language |
English |
format |
eBook |
author |
Tsirikoglou, Apostolia. |
spellingShingle |
Tsirikoglou, Apostolia. Synthetic Data for Visual Machine Learning : A Data-Centric Approach. Linköping Studies in Science and Technology. Licentiate Thesis Series ; Intro -- Abstract -- Populärvetenskaplig Sammanfattning -- Acknowledgments -- List of Publications -- Contributions -- Contents -- 1 Introduction -- 1.1 Visual data -- 1.2 Image synthesis -- 1.2.1 Computer graphics -- 1.2.2 Generative image modeling -- 1.3 Deep learning -- 1.3.1 Training data -- 1.4 Objectives -- 1.5 Outline -- 2 Background -- 2.1 Deep learning -- 2.1.1 Neural networks -- 2.1.2 Basic concepts -- 2.1.3 Applications -- 2.2 Computer vision -- 2.3 Digital pathology -- 3 Computer graphics -- 3.1 Modeling -- 3.1.1 Basics -- 3.1.2 Common practices -- 3.1.3 Procedural modeling -- 3.2 Rendering -- 3.2.1 Light transport theory -- 3.2.2 Light transport simulation -- 4 Generative modeling -- 4.1 Fundamentals -- 4.2 Deep generative models -- 4.3 Generative adversarial networks -- 4.3.1 Challenges -- 4.3.2 Common variants -- 5 Synthetic data for deep learning -- 5.1 Data-centric AI -- 5.1.1 Common practices -- 5.2 Data collection -- 5.2.1 Discussion -- 5.3 Data generation -- 5.3.1 Computer graphics -- 5.3.2 Generative adversarial networks -- 5.3.3 Contributions -- 5.3.4 Discussion -- 5.4 Data augmentation -- 5.4.1 Image manipulations -- 5.4.2 Deep learning approaches -- 5.4.3 Contributions -- 5.4.4 Discussion -- 6 Conclusion -- 6.1 Contributions -- 6.1.1 Data generation -- 6.1.2 Data augmentation -- 6.2 Discussion -- 7 Outlook -- Bibliography -- Papers. |
author_facet |
Tsirikoglou, Apostolia. |
author_variant |
a t at |
author_sort |
Tsirikoglou, Apostolia. |
title |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
title_sub |
A Data-Centric Approach. |
title_full |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
title_fullStr |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
title_full_unstemmed |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
title_auth |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
title_new |
Synthetic Data for Visual Machine Learning : |
title_sort |
synthetic data for visual machine learning : a data-centric approach. |
series |
Linköping Studies in Science and Technology. Licentiate Thesis Series ; |
series2 |
Linköping Studies in Science and Technology. Licentiate Thesis Series ; |
publisher |
Linkopings Universitet, |
publishDate |
2022 |
physical |
1 online resource (144 pages) |
edition |
1st ed. |
contents |
Intro -- Abstract -- Populärvetenskaplig Sammanfattning -- Acknowledgments -- List of Publications -- Contributions -- Contents -- 1 Introduction -- 1.1 Visual data -- 1.2 Image synthesis -- 1.2.1 Computer graphics -- 1.2.2 Generative image modeling -- 1.3 Deep learning -- 1.3.1 Training data -- 1.4 Objectives -- 1.5 Outline -- 2 Background -- 2.1 Deep learning -- 2.1.1 Neural networks -- 2.1.2 Basic concepts -- 2.1.3 Applications -- 2.2 Computer vision -- 2.3 Digital pathology -- 3 Computer graphics -- 3.1 Modeling -- 3.1.1 Basics -- 3.1.2 Common practices -- 3.1.3 Procedural modeling -- 3.2 Rendering -- 3.2.1 Light transport theory -- 3.2.2 Light transport simulation -- 4 Generative modeling -- 4.1 Fundamentals -- 4.2 Deep generative models -- 4.3 Generative adversarial networks -- 4.3.1 Challenges -- 4.3.2 Common variants -- 5 Synthetic data for deep learning -- 5.1 Data-centric AI -- 5.1.1 Common practices -- 5.2 Data collection -- 5.2.1 Discussion -- 5.3 Data generation -- 5.3.1 Computer graphics -- 5.3.2 Generative adversarial networks -- 5.3.3 Contributions -- 5.3.4 Discussion -- 5.4 Data augmentation -- 5.4.1 Image manipulations -- 5.4.2 Deep learning approaches -- 5.4.3 Contributions -- 5.4.4 Discussion -- 6 Conclusion -- 6.1 Contributions -- 6.1.1 Data generation -- 6.1.2 Data augmentation -- 6.2 Discussion -- 7 Outlook -- Bibliography -- Papers. |
isbn |
9789179291754 9789179291747 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30229654 |
illustrated |
Not Illustrated |
oclc_num |
1366125803 |
work_keys_str_mv |
AT tsirikoglouapostolia syntheticdataforvisualmachinelearningadatacentricapproach |
status_str |
n |
ids_txt_mv |
(MiAaPQ)50030229654 (Au-PeEL)EBL30229654 (OCoLC)1366125803 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2202 |
is_hierarchy_title |
Synthetic Data for Visual Machine Learning : A Data-Centric Approach. |
container_title |
Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2202 |
marc_error |
Info : MARC8 translation shorter than ISO-8859-1, choosing MARC8. --- [ 856 : z ] |
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