Convolutional Neural Networks

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Table of contents

1 Introduction
1.1 Background and problem motivation
1.2 Problem Description
1.3 Tools, Requirements
1.4 Milestones
1.5 Overall Aim
1.6 Goals
1.7 Scope
1.8 Outline
1.9 Contribution
2 Theory
2.1 Machine Learning
2.2 Neural Networks
2.2.1 Neural network architecture
2.2.2 Neural network training
2.3 Convolutional Neural Networks
2.4 Generative Adversarial Networks
2.4.1 GANs architecture
2.4.2 GANs training
2.5 Depth Image-Based Rendering
2.6 Related Work
3 Methodology
3.1 Data Preparation
3.2 GAN Training
Disocclusion Inpainting using
Generative Adversarial Networks
Nadeem Aftab
3.3 Disocclusion inpainting with GAN
3.4 Inpainting performance measure
3.5 Analysis of results
3.6 Libraries
3.7 Hardware
4 Implementation
4.1 Data Preparation Implementation
4.2 GAN Implementation and training
4.2.1 Generator Implementation
4.2.2 Discriminator Implementation
4.2.3 GAN Training
4.3 Inpainting on prepared data with GAN Implementation
4.4 Inpainting performance measure
4.5 Implementation for analysis of results
5 Results
5.1 DIBR generated data
5.2 GAN Results
5.3 Inpainting Method Results
5.4 Performance measure Results
5.5 Analysis of Results
6 Conclusion
6.1 Evaluation according to goals
6.2 Conclusion of inpainting disocclusion
6.3 Future Work
6.4 Ethical and Social Impact
References