Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

While deep learning (DL) is data-hungry and usually relies on extensive labeled data to
deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small …

Active learning (AL) methods have been proven to be an effective way to reduce the labeling
effort by intelligently selecting valuable instances for annotation. Despite their great success …

Active learning (AL) is to design label-efficient algorithms by labeling the most
representative samples. It reduces annotation cost and attracts increasing attention from the …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but
its success hinges on obtaining large amounts of precise 3D annotations. Active learning …

Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few
active learning studies have attempted to deal with this open-set noise for sample selection …

As an important data selection schema, active learning emerges as the essential component
when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the …

Active Learning (AL) aims to enhance the performance of deep models by selecting the most
informative samples for annotation from a pool of unlabeled data. Despite impressive …

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is
a promising solution that learns to select only a small portion of unlabeled data to annotate …