Informative Sample

Informative sample selection and optimization are crucial for improving the efficiency and accuracy of various machine learning models. Current research focuses on developing strategies to identify and utilize the most informative samples for training, including active learning techniques and methods to enhance the quality of pseudo-labels from unlabeled data. This is particularly relevant for tasks with limited labeled data, such as agricultural mapping and monocular 3D object detection, where improving sample informativeness directly translates to better model performance. The resulting advancements have significant implications for resource-constrained applications and the development of more robust and efficient machine learning systems.