Uncertainty Based Active Learning

Uncertainty-based active learning (UAL) aims to efficiently train machine learning models by strategically selecting the most informative data points for labeling, minimizing annotation costs. Current research focuses on improving UAL's robustness to model mismatches and calibration issues, often employing Bayesian neural networks, ensemble methods, and techniques like Low-Rank Adaptation (LoRA) for large language models. These advancements are particularly impactful in resource-constrained domains like medical imaging and robotics, enabling faster and more cost-effective model development for applications requiring high accuracy with limited labeled data.