In the realm of active learning, query strategies are techniques employed to identify and select the most informative samples for human annotators to label. The primary objective is to enhance the accuracy of machine learning models. By focusing on choosing samples that carry the most valuable information, these strategies strive to minimize the overall number of samples that need to be labeled. This not only optimizes the labeling process but also significantly reduces the associated costs.
