Deep Learning models' performance strongly correlate with availability of annotated data;
however, massive data labeling is laborious, expensive, and error-prone when performed by …

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 select the most useful data samples from an unlabeled data
pool and annotate them to expand the labeled dataset under a limited budget. Especially …

Active learning (AL) aims to sample the most valuable data for model improvement from the
unlabeled pool. Traditional works, especially uncertainty-based methods, are prone to suffer …

Deep active learning (DeepAL) offers a viable solution for enhancing the predictive
performance of models with limited annotation costs. Popular DeepAL pipelines fail to (1) …

Deep predictive models rely on human supervision in the form of labeled training data.
Obtaining large amounts of annotated training data can be expensive and time consuming …

Obtaining labeled data for machine learning tasks can be prohibitively expensive. Active
learning mitigates this issue by exploring the unlabeled data space and prioritizing the …

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 …

Given an unlabeled pool of data and the experts who can label them, active learning aims to
build an agent that can effectively acquire data to be queried to the experts, maximizing the …

Active learning, reducing the cost and workload of annotations, attracts increasing attentions
from the community. Current active learning approaches commonly adopted uncertainty …