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 …

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 …

Requirement of large annotated datasets restrict the use of deep convolutional neural
networks (CNNs) for many practical applications. The problem can be mitigated by using …

Active learning is to design label-efficient algorithms by sampling the most representative
samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial …

The randomized or cross-validated split of training and testing sets has been adopted as the
gold standard of machine learning for decades. The establishment of these split protocols …

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 …

Annotating datasets for object detection is an expensive and time-consuming endeavor. To
minimize this burden active learning (AL) techniques are employed to select the most …

Central to active learning (AL) is what data should be selected for annotation. Existing works
attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains …

Given an unlabeled dataset and an annotation budget, we study how to selectively label a
fixed number of instances so that semi-supervised learning (SSL) on such a partially labeled …

Large-scale datasets have played a crucial role in the advancement of computer vision.
However, they often suffer from problems such as class imbalance, noisy labels, dataset …