Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

The ability to train complex and highly effective models often requires an abundance of
training data, which can easily become a bottleneck in cost, time, and computational …

The promise of active learning (AL) is to reduce labelling costs by selecting the most
valuable examples to annotate from a pool of unlabelled data. Identifying these examples is …

Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …

The unsupervised detection and localization of anomalies in natural images is an intriguing
and challenging problem. Anomalies manifest themselves in very different ways and an …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Despite the substantial progress of active learning for image recognition, there still lacks an
instance-level active learning method specified for object detection. In this paper, we …

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 …

Despite the power of deep neural networks for a wide range of tasks, an overconfident
prediction issue has limited their practical use in many safety-critical applications. Many …