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 …

Geophysical logging series are valuable geological data that record the physical and
chemical information of borehole walls and in-situ formations, and are widely used by …

Active learning can query valuable samples in an unlabeled sample pool for annotation,
thus building a more informative labeled dataset and reducing the annotation cost. However …

modAL is a modular active learning framework for Python, aimed to make active learning
research and practice simpler. Its distinguishing features are (i) clear and modular object …

Active learning (AL) aims to select valuable samples for labeling from an unlabeled sample
pool to build a training dataset with minimal annotation cost. Traditional methods always …

Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks.
However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever …

Active learning (AL) for object detection (OD) aims to reduce labeling costs by selecting the
most valuable samples that enhance the detection network from the unlabeled pool. Due to …

Data generation and labeling are often expensive in robot learning. Preference-based
learning is a concept that enables reliable labeling by querying users with preference …

Recent research has shown promising results by using machine learning (ML) techniques to
improve the performance of database systems, eg, in query optimization or index …

Active learning has gained attention as a method to expedite the learning curve of
classifiers. To this end, uncertainty sampling is a widely adopted strategy that selects …