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Introduction

Zero-shot learning is a promising technique in machine learning, where the model is trained on a set of classes but is able to generalize to unseen classes at test time. This approach is crucial in real-world applications where the number of classes is constantly evolving, and it is not feasible to collect data for all possible classes. Zero-shot learning for unseen classes has garnered significant interest in recent years due to its potential to address the challenges of scalability and adaptability in machine learning systems.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem Statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to zero-shot learning
2.2 Zero-shot learning models
2.3 Applications of zero-shot learning
2.4 Challenges in zero-shot learning
2.5 Advantages of zero-shot learning
2.6 Comparison with other machine learning techniques
2.7 Zero-shot learning datasets
2.8 Evaluation metrics for zero-shot learning
2.9 Future directions in zero-shot learning
2.10 Summary of the literature review

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data preprocessing
3.3 Feature extraction
3.4 Attribute representation
3.5 Zero-shot learning algorithm selection
3.6 Model training
3.7 Model evaluation
3.8 Performance analysis
3.9 Experimental setup
3.10 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Implementation of the zero-shot learning model
4.2 Integration of the system components
4.3 Testing and validation
4.4 Performance tuning
4.5 Deployment on test datasets
4.6 Results interpretation
4.7 Comparison with existing methods
4.8 Discussion of implementation challenges
4.9 Summary of system implementation

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

Thesis Overview on Zero-shot Learning for Unseen Classes

Zero-shot learning for unseen classes is a cutting-edge approach that aims to address the challenges of scalability and adaptability in machine learning systems. In this thesis, we delve into the intricacies of zero-shot learning, its applications, challenges, advantages, and future directions. The study encompasses a comprehensive literature review, system design, methodology, implementation, and concludes with a summary of key findings and suggestions for future research.

Chapter 1 provides an in-depth introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms related to zero-shot learning for unseen classes. Chapter 2 presents a detailed literature review on zero-shot learning, covering various aspects such as models, applications, datasets, evaluation metrics, challenges, advantages, and future directions.

Chapter 3 delves into the system design and methodology, discussing the system architecture, data preprocessing, feature extraction, attribute representation, algorithm selection, model training, evaluation, performance analysis, and experimental setup. Chapter 4 focuses on the system implementation, including the implementation of the zero-shot learning model, integration of system components, testing, validation, performance tuning, deployment, results interpretation, comparison with existing methods, and discussion of implementation challenges.

Chapter 5 wraps up the thesis with a conclusion, summary of key findings, contributions of the study, limitations, future research directions, and final remarks. This thesis aims to provide a comprehensive understanding of zero-shot learning for unseen classes and contribute to the advancement of machine learning techniques for real-world applications.

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