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Introduction:

Weakly supervised learning is a subfield of machine learning that aims to train models using data with noisy or incomplete labels. This is a common scenario in many real-world applications where obtaining accurately labeled data can be expensive or time-consuming. Noisy labels, which are incorrectly assigned labels, can significantly impact the performance of machine learning models. In this thesis, we will focus on Weakly supervised learning for noisy labels, exploring different strategies to improve the accuracy and robustness of models trained with imperfectly labeled data.

Table of Contents:

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 Weakly supervised learning
2.2 Types of noisy labels
2.3 Challenges of training models with noisy labels
2.4 Existing approaches to handling noisy labels
2.5 Weakly supervised learning algorithms
2.6 Evaluation metrics for noisy labels
2.7 Applications of Weakly supervised learning
2.8 Recent advancements in Weakly supervised learning
2.9 Comparison of different approaches
2.10 Gaps in the existing literature

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Noise detection and correction techniques
3.3 Weakly supervised learning algorithms selection
3.4 Model training and evaluation
3.5 Hyperparameter tuning
3.6 Performance comparison with baseline models
3.7 Model interpretability techniques
3.8 Cross-validation and robustness testing

Chapter 4: System Implementation
4.1 Implementation of data preprocessing pipeline
4.2 Integration of noise detection and correction techniques
4.3 Implementation of Weakly supervised learning algorithms
4.4 Training and evaluation of models
4.5 Visualization of model performance
4.6 Performance optimization
4.7 Deployment and scalability considerations
4.8 Testing and validation

Chapter 5: Conclusion and Summary
5.1 Recap of research objectives
5.2 Summary of key findings
5.3 Contributions to the field
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview:

Weakly supervised learning for noisy labels is a challenging yet essential aspect of machine learning research. This thesis aims to investigate different strategies and techniques to improve the accuracy and robustness of models trained with imperfectly labeled data. Chapter 1 provides an introduction to the topic, outlining the background, problem statement, objectives, scope, and significance of the study. Chapter 2 presents a comprehensive literature review, summarizing existing approaches, algorithms, evaluation metrics, applications, and recent advancements in Weakly supervised learning. Chapter 3 details the system design and methodology, including data collection, preprocessing, noise detection, correction, model selection, training, evaluation, hyperparameter tuning, and performance comparison. Chapter 4 focuses on the system implementation, covering data preprocessing pipeline, noise detection and correction techniques, model training, evaluation, visualization, performance optimization, deployment, scalability, testing, and validation. Finally, Chapter 5 concludes the thesis with a summary of key findings, contributions to the field, recommendations for future research, and a conclusion. This thesis aims to contribute to the advancement of Weakly supervised learning for noisy labels and provide valuable insights for researchers and practitioners in the field.

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