Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

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 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 …

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 …

Unsupervised domain adaptation has recently emerged as an effective paradigm for
generalizing deep neural networks to new target domains. However, there is still enormous …

Active learning strives to reduce annotation costs by choosing the most critical examples to
label. Typically, the active learning strategy is contingent on the classification model. For …

Intense interest in applying convolutional neural networks (CNNs) in biomedical image
analysis is wide spread, but its success is impeded by the lack of large annotated datasets in …

Labelling point clouds fully is highly time-consuming and costly. As larger point cloud
datasets with billions of points become more common, we ask whether the full annotation is …

We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural
networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …