Synthetic Data for Visual Machine Learning : : A Data-Centric Approach.

• Superior document: Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2202
• Author: Tsirikoglou, Apostolia.
• 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
• Physical Description: 1 online resource (144 pages)

Table of 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.