Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/abdelrahman-amen/active_learning_with_different_query_strategies

This project explores the implementation of active learning techniques, focusing on various query strategies to optimize the selection of informative data points for model training. It aims to reduce the amount of labeled data required while improving model performance, especially in scenarios with limited labeled data.
https://github.com/abdelrahman-amen/active_learning_with_different_query_strategies

activelearning cuda entropy kldivergence margin numpy python pyto uncertainty

Last synced: 20 days ago
JSON representation

This project explores the implementation of active learning techniques, focusing on various query strategies to optimize the selection of informative data points for model training. It aims to reduce the amount of labeled data required while improving model performance, especially in scenarios with limited labeled data.

Awesome Lists containing this project

README 

        
# Active_Learning_with_different_Query_Strategies 🪐



# Introduction 🔍 

Active learning is a machine learning approach where the model selects the most informative, unlabeled data points to be labeled by an oracle (usually a human). This method is valuable when labeled data is scarce or expensive, allowing the model to perform well with fewer labeled examples.



# Benefits of Active Learning 🎯



• Less Labeled Data: Active learning can significantly reduce the number of labeled data points required to train an effective model, saving time and cost.



• Better Performance: By focusing on the most informative examples, active learning often leads to better performance than using random sampling.



• Reduces Overfitting: The model can generalize better, as it learns from more diverse, informative examples rather than just random ones.



# Applications 🧠



• Natural Language Processing (NLP): In tasks like text classification, sentiment analysis, and named entity recognition, active learning can help select the most relevant documents to label.



• Computer Vision: Active learning is used for object detection, image classification, and semantic segmentation by selecting images that maximize model uncertainty.



• Speech Recognition: Active learning can be used to select the most uncertain or informative speech samples, improving recognition models with fewer annotated audio clips.



# Objectives of Active Learning 🎯



• Reduce Data Labeling Costs: By minimizing the number of labeled samples needed, active learning cuts down on the overall cost and time required for annotation.



• Improve Model Efficiency: It helps the model learn more from fewer data points, making training more efficient.



• Improve Accuracy: Active learning helps increase the performance of models in specific domains with limited data.



• Enable Learning from Small Datasets: Particularly useful in situations where gathering labeled data is challenging or expensive, like medical imaging or rare event prediction.



# Active Learning Scenarios 📊



• Pool-Based Active Learning: A large pool of unlabeled data is available, and the model selects the most informative samples from it for labeling.



• Membership Query Synthesis: The model generates queries about data points it would like to label, creating new data samples for annotation.



• Stream-Based Sampling: In real-time systems, data arrives continuously, and the model selects data points from the stream that are most useful for learning.



# Strategies for Active Learning 💡



• Low Confidence Sampling: The model queries labels for samples where it has the least confidence in its prediction. These are the data points that the model is most uncertain about.



• Margin-Based Sampling: Active learning chooses the samples that lie closest to the decision boundary. These points are often the hardest for the model to classify correctly, making them valuable for improving the model.



• Entropy-Based Sampling: The model selects the data points with the highest uncertainty or entropy, where the model's prediction probabilities are spread out the most.



# Conclusion 📝



Active learning is a powerful machine learning technique that can be used to improve the

performance of models with less training data. Active learning is particularly useful in

applications where labeled data is expensive or difficult to obtain.



# Want More Details? 📄

If you're interested in a deeper dive, check out my full report here!  

[Click here to view the report](https://github.com/Abdelrahman-Amen/Active_Learning_with_different_Query_Strategies/blob/main/Technical%20report.pdf)