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Thesis Overview:

Title: Generative Adversarial Networks for Video Prediction
Introduction:
Generative adversarial networks (GANs) have gained significant attention in recent years for their ability to generate realistic data samples. Video prediction is a challenging task that requires predicting future frames in a video sequence. In this thesis, we explore the use of GANs for video prediction and examine their effectiveness in generating accurate future frames.

Chapter 1: Introduction
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 Overview of Generative Adversarial Networks
2.2 Video Prediction Techniques
2.3 GANs for Image Generation
2.4 GANs for Video Generation
2.5 Applications of GANs in Computer Vision
2.6 Challenges in Video Prediction
2.7 Evaluation Metrics for Video Prediction
2.8 Related Work in Video Prediction
2.9 Advantages and Disadvantages of GANs in Video Prediction
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Preprocessing of Video Data
3.3 GAN Architecture for Video Prediction
3.4 Training Process
3.5 Evaluation Metrics
3.6 Experiment Design
3.7 Comparison with Baseline Models
3.8 Statistical Analysis
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of GANs for Video Prediction
4.2 Analysis of Generated Frames
4.3 Comparison with State-of-the-Art Models
4.4 Interpretation of Results
4.5 Limitations of the Study
4.6 Future Research Directions
4.7 Implications for Video Prediction
4.8 Practical Applications of GANs in Video Generation

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

Overall, this thesis aims to provide insights into the application of GANs for video prediction and evaluate their performance in generating accurate future frames. By examining the literature, conducting experiments, and analyzing the results, we aim to contribute to the advancement of video prediction techniques using GANs.

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