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Introduction
Generating realistic images has long been a challenging problem in the field of computer vision. Generative Adversarial Networks (GANs) have emerged as a powerful tool for image generation, allowing for the creation of high-quality and diverse images that are visually indistinguishable from real images. In this thesis, we aim to build a GAN-based model for image generation, exploring the potential of this technology in various applications such as image synthesis, image editing, and data augmentation.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Generative Adversarial Networks
2.2 History and Evolution of GANs
2.3 Applications of GANs in Image Generation
2.4 Challenges and Limitations of GANs
2.5 State-of-the-art GAN Architectures
2.6 Evaluation Metrics for Image Generation
2.7 Image Dataset Preparation for GAN Training
2.8 Training Strategies for GANs
2.9 Recent Advancements in GAN Research
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 Introduction to System Design
3.2 Selection of GAN Architecture
3.3 Data Preprocessing Techniques
3.4 Implementation of Loss Functions
3.5 Training Process and Hyperparameter Tuning
3.6 Evaluation Metrics for Image Quality
3.7 Data Augmentation Techniques
3.8 Performance Analysis
3.9 Comparison with Existing GAN Models

Chapter 4: System Implementation
4.1 Introduction to System Implementation
4.2 Data Collection and Preparation
4.3 GAN Model Implementation
4.4 Training Procedure
4.5 Evaluation of Generated Images
4.6 Fine-tuning and Optimization
4.7 Scalability and Efficiency
4.8 Hardware and Software Requirements
4.9 Results and Discussion

Chapter 5: Conclusion and Summary
5.1 Conclusion
5.2 Summary of Findings
5.3 Contributions of the Study
5.4 Future Directions for Research
5.5 Conclusion Remarks

Thesis Overview:
Generative Adversarial Networks (GANs) have gained significant attention in recent years for their ability to generate realistic images. This thesis focuses on building a GAN-based model for image generation, exploring the potential of this technology in various applications such as image synthesis, image editing, and data augmentation. The thesis is structured into five chapters, each addressing different aspects of the research study.

Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on GANs, covering topics such as GAN architecture, applications in image generation, challenges, state-of-the-art architectures, evaluation metrics, dataset preparation, training strategies, and recent advancements.

Chapter 3 delves into the system design and methodology, discussing the selection of GAN architecture, data preprocessing techniques, implementation of loss functions, training procedures, evaluation metrics, data augmentation techniques, and performance analysis. Chapter 4 focuses on the system implementation, detailing data collection and preparation, GAN model implementation, training procedures, evaluation of generated images, fine-tuning, optimization, scalability, efficiency, and hardware/software requirements.

Finally, Chapter 5 presents the conclusion and summary of the study, highlighting the main findings, contributions, future research directions, and concluding remarks. This thesis aims to advance the field of image generation using GANs and contribute to the growing body of research in this area.

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