Results 11 to 20 of about 3,036,262 (193)
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
IEEE Transactions on Medical Imaging, 2022 While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven ...
Zhao, Ziyuan +5 more
core +5 more sources
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons.
Zimmermann, Karel +4 more
core +5 more sources
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation [PDF]
Advances in Neural Information Processing Systems 36, 2023 Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) that does not generalize to the target domain.
Zhang, Hanwang +2 more
core +5 more sources
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling [PDF]
Proceedings of the AAAI Conference on Artificial IntelligenceCryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological ...
Kihara, D +26 more
core +8 more sources
Unsupervised domain adaptation with post-adaptation labeled domain performance preservation
Machine Learning with Applications, 2022 Unsupervised domain adaptation is a machine learning-oriented application that aims to transfer knowledge learned from a seen (source) domain with labeled data to an unseen (target) domain with only unlabeled data.
Haidi Badr, Nayer Wanas, Magda Fayek
doaj +2 more sources
MeTa Learning-Based Optimization of Unsupervised Domain Adaptation Deep Networks
MathematicsThis paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation.
Hsiau-Wen Lin +4 more
doaj +2 more sources
Diffusion‐UDA: Diffusion‐based unsupervised domain adaptation for submersible fault diagnosis
Electronics LettersDeep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its ...
Penghui Zhao +5 more
doaj +2 more sources
In this work, we take a deeper look into the diverse factors that influence the efficacy of modern unsupervised domain adaptation (UDA) methods using a large-scale, controlled empirical study.
Ravichandran, Sreyas +2 more
core +4 more sources
CoRRSegmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Language Models (VLMs) and leveraging synthetic data.
Alcover-Couso, Roberto +3 more
core +4 more sources
Test-Time Unsupervised Domain Adaptation [PDF]
, 2020 Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation.
Orbes-Arteaga, Mauricio +11 more
core +3 more sources