Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field.
arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.
By Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar Sinha
arXiv:2608. 12035v1 Announce Type: cross Abstract: Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship.
By Yiheng Xiong, Luisa Gall\'ee, Daniel Santak Wolf, Heiko Hillenhagen, Michael G\"otz
arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.
By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah
arXiv:2607. 28125v2 Announce Type: replace-cross Abstract: Numerous unsupervised domain adaptation (UDA) algorithms exist, but for clinical practice, selecting the best-suited one along with proper hyperparameters often remains unclear, as the unlabeled deployment (target) domain prevents direct evaluation.
By Yiheng Xiong, Luisa Gall\'ee, Daniel Santak Wolf, Heiko Hillenhagen, Michael G\"otz
arXiv:2406.09896v3 Announce Type: replace
Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safe...
By Brun\'o B. Englert, Fabrizio J. Piva, Tommie Kerssies, Daan de Geus, Gijs Dubbelman