Digital twins (DTs) are an emerging digitalization technology with a huge impact on today's
innovations in both industry and research. DTs can significantly enhance our society and …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Abstract Recently, Neural Radiance Fields (NeRF) has shown promising performances on
reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images …

Investigating active learning, we focus on the relation between the number of labeled
examples (budget size), and suitable querying strategies. Our theoretical analysis shows a …

Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a
popular paradigm in multiple computer vision tasks. Previous research has covered both the …

Deep active learning aims to reduce the annotation cost for the training of deep models,
which is notoriously data-hungry. Until recently, deep active learning methods were …

Labeling a large set of data is expensive. Active learning aims to tackle this problem by
asking to annotate only the most informative data from the unlabeled set. We propose a …

Active Learning has emerged as a viable solution for addressing the challenge of labeling
extensive amounts of data in data-intensive applications such as computer vision and neural …

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 …

The performance of learning-based algorithms improves with the amount of labelled data
used for training. Yet, manually annotating data is particularly difficult for medical image …