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Introduction

Variational autoencoders (VAEs) have gained significant attention in the field of generative modeling due to their ability to learn complex distributions and generate realistic samples. This thesis aims to explore the use of VAEs for generative modeling and investigate their effectiveness in capturing the underlying data distribution.

Background of Study

Generative modeling is a fundamental task in machine learning, with applications ranging from image generation to natural language processing. Traditional generative models like Gaussian mixture models and autoregressive models have limitations in capturing complex data distributions. VAEs, on the other hand, offer a promising approach by learning a latent representation of the data and generating samples from this distribution.

Problem Statement

Despite their potential, VAEs face challenges in training and generating high-quality samples. Understanding these challenges and developing solutions to improve the performance of VAEs is crucial for their widespread adoption in generative modeling tasks.

Objective of Study

The primary objective of this study is to investigate the use of VAEs for generative modeling and explore ways to enhance their performance in capturing complex data distributions. Specific objectives include studying different VAE architectures, optimizing hyperparameters, and evaluating the generated samples quantitatively.

Limitation of Study

This study is limited to investigating VAEs for generative modeling tasks in image and text data. The findings may not be generalizable to other data types or domains.

Scope of Study

The scope of this study includes a comprehensive review of VAEs for generative modeling, experimental evaluation on benchmark datasets, and analysis of the generated samples. The study will focus on understanding the limitations of VAEs and proposing possible solutions to overcome them.

Significance of Study

This study contributes to the growing body of research on VAEs for generative modeling by providing insights into their strengths and limitations. The findings can help researchers and practitioners in effectively applying VAEs to various generative modeling tasks.

Structure of the Thesis

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 Introduction to generative modeling
2.2 Traditional generative models
2.3 Introduction to VAEs
2.4 VAE architectures
2.5 Training VAEs
2.6 Evaluating VAEs
2.7 Challenges in VAEs
2.8 Improvements in VAEs
2.9 Applications of VAEs
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 Introduction to system design
3.2 Data preprocessing
3.3 VAE architecture design
3.4 Hyperparameter optimization
3.5 Training process
3.6 Evaluation metrics
3.7 Experimental setup
3.8 Performance analysis
3.9 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Introduction to system implementation
4.2 Implementation details
4.3 Code optimization
4.4 Testing and debugging
4.5 Results visualization
4.6 Model interpretation
4.7 Performance comparison
4.8 Summary of system implementation

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions of the study
5.3 Limitations of the study
5.4 Future research directions
5.5 Conclusion

Thesis Overview on Variational Autoencoders for Generative Modeling

Generative modeling is a fundamental task in machine learning, with applications in image generation, text generation, and anomaly detection. Traditional generative models have limitations in capturing complex data distributions and generating realistic samples. Variational autoencoders (VAEs) offer a promising approach by learning a latent representation of the data and generating samples from this distribution.

This thesis explores the use of VAEs for generative modeling and investigates their effectiveness in capturing complex data distributions. The study includes a comprehensive review of VAE architectures, training strategies, hyperparameter optimization, and evaluation metrics. Experimental evaluations are conducted on benchmark datasets to assess the performance of VAEs in generating high-quality samples.

The findings of this study contribute to the understanding of VAEs for generative modeling tasks and provide insights into their strengths and limitations. The study aims to bridge the gap between theoretical understanding and practical applications of VAEs in real-world scenarios. Future research directions are proposed to further enhance the performance of VAEs and explore novel applications in generative modeling.

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