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 …

Supervised machine learning methods are increasingly employed in political science. Such
models require costly manual labeling of documents. In this paper, we introduce active …

The performance of deep neural networks improves with more annotated data. The problem
is that the budget for annotation is limited. One solution to this is active learning, where a …

Various strategies for active learning have been proposed in the machine learning literature.
In uncertainty sampling, which is among the most popular approaches, the active learner …

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 …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

The existing active learning methods select the samples by evaluating the sample's
uncertainty or its effect on the diversity of labeled datasets based on different task-specific or …

Retinal fundus image analysis (RFIA) for diabetic retinopathy (DR) screening can be used to
reduce the risk of blindness among diabetic patients. The RFIA screening programs help the …

Active learning (AL) combines data labeling and model training to minimize the labeling cost
by prioritizing the selection of high value data that can best improve model performance. In …

Active domain adaptation (ADA) aims to improve the model adaptation performance by
incorporating the active learning (AL) techniques to label a maximally-informative subset of …