Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have
drawn growing attention due to promising performances for target scenarios. Most existing …

Abstract Domain adaptation typically requires to access source domain data to utilize their
distribution information for domain alignment with the target data. However, in many real …

(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …

In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes
pulse sequence-based contrast variations in MR images from site to site, which impedes …

Deep neural networks have been successfully applied to medical image analysis tasks like
segmentation and synthesis. However, even if a network is trained on a large dataset from …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Although current face anti-spoofing methods achieve promising results under intra-dataset
testing, they suffer from poor generalization to unseen attacks. Most existing works adopt …

Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …