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Introduction:
Generative adversarial networks (GANs) have gained significant attention in recent years for their ability to generate realistic images from text descriptions. In the field of text-to-image synthesis, GANs have shown promising results in generating high-quality images that are indistinguishable from real images. This thesis aims to explore the potential of GANs for text-to-image synthesis and investigate the challenges and limitations in this area.

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
– Overview of generative adversarial networks
– Text-to-image synthesis techniques
– Previous research on GANs for text-to-image synthesis
– Challenges in text-to-image synthesis using GANs

Chapter 3: Research Methodology
– Data collection and preprocessing
– GAN architecture selection
– Training process
– Evaluation metrics
– Experiment design
– Hyperparameter tuning
– Benchmark datasets
– Comparison with other methods

Chapter 4: Discussion of Findings
– Analysis of experimental results
– Comparison with state-of-the-art methods
– Discussion on the limitations and improvements
– Future research directions

Chapter 5: Conclusion and Summary
– Recap of key findings
– Contributions of the study
– Implications for future research
– Conclusion

Thesis Overview:

Generative adversarial networks (GANs) have emerged as a powerful tool for generating realistic images from textual descriptions. This thesis explores the application of GANs for text-to-image synthesis, aiming to investigate the challenges, limitations, and potential advancements in this field.

Chapter 1 provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, scope, significance, thesis structure, and definition of terms related to text-to-image synthesis using GANs.

In Chapter 2, a comprehensive literature review is presented, covering the fundamentals of GANs, text-to-image synthesis techniques, previous research works, and challenges in using GANs for text-to-image synthesis.

Chapter 3 details the research methodology, including data collection, preprocessing, GAN architecture selection, training process, evaluation metrics, experiment design, hyperparameter tuning, and benchmark datasets.

Chapter 4 contains a thorough discussion of the findings, including an analysis of experimental results, comparison with state-of-the-art methods, discussions on limitations and future research directions.

Finally, Chapter 5 presents the conclusion and summary of the thesis, recapping key findings, highlighting contributions, outlining implications for future research, and concluding the study on Generative adversarial networks for text-to-image synthesis.

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