<ul><li>This paper gives an overview of Active Learning (AL) in machine learning, which helps models achieve better performance using fewer labeled examples.</li><li>AL is used in various fields such as computer vision, natural language processing, transfer learning, and real-world applications.</li><li>The paper focuses on topics like uncertainty estimation, handling class imbalance, fairness, and creation of strong evaluation metrics.</li><li>AL often gives better results than passive learning, especially with good evaluation measures.</li></ul>

Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement

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