… deep learning models have been developed to segment anatomical structures from medical
images, … a novel framework for unsupervised domain adaptation. We disentangled images …

… State-of-the-art machine learning methods have … unsupervised domain adaptation on large
domain shifts such as from COCO to biomedical images, where such image-level adaptation …

… Unsupervised domain adaptation (UDA) aims to generalize the supervised model trained on
a source domain to an unlabeled target domain. … a Deep Conditional Adaptation Network (…

… on commonly used unsupervised domain adaptation datasets and their problems. For this,
we first establish the most popular datasets by inspecting 14 recent CVPR 2020 publications …

… To model such linear dependency, we propose to train a deep neural network with a novel
… modeling for unsupervised domain adaptation: Application to x-ray image segmentation. In …

… methods used to tackle the domain shift problem in machine learning and computer vision.
… unsupervised domain adaptation in the context of semantic segmentation of medical images…

… annotations for a single task and the advances in high-performance computing. However, …
pretrained model for 3-D medical image analysis tasks. Domain adaptation is a form of TL in …

… (target) images obtained differently than its (source) training data. Due to a lack of target
label data, most work has focused on unsupervised domain adaptation (UDA). Current UDA …

… directly applied in the partial domain adaptation cases due to the … a deep learning-based
partial domain adaptation method for … The recent advances on the cross-domain fault diagnostic …

… be solved using Unsupervised Domain Adaptation (UDA) … , but for any deep learning
system trained with single image … resentations for domain adaptation,” in Advances in neural …