Results 41 to 50 of about 4,969,023 (298)
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective [PDF]
Computer Vision and Pattern Recognition, 2023 Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment.
Jinjing Zhu, Haotian Bai, Lin Wang
semanticscholar +1 more source
Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations
IEEE Access, 2022 Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, e.g., images and videos, but is not utilized for
Dongmin Kang +3 more
doaj +1 more source
Discriminative Radial Domain Adaptation
IEEE Transactions on Image Processing, 2023 13 pages, 14 ...
Zenan Huang +4 more
openaire +3 more sources
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2023 Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a labeled source domain to an unlabeled target domain. In contrast to UDA, source-free domain adaptation (SFDA) is a more practical setup as access to source data is no ...
Nazmul Karim +5 more
semanticscholar +1 more source
DACS: Domain Adaptation via Cross-domain Mixed Sampling [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020 Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going ...
Wilhelm Tranheden +3 more
semanticscholar +1 more source
Self Domain Adapted Network [PDF]
, 2020 Domain shift is a major problem for deploying deep networks in clinical practice. Network performance drops significantly with (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).
He, Yufan +4 more
openaire +2 more sources
IEEE Access, 2021 Domain adaptation (DA) is a technology that transfers knowledge from the source domain to the target domain. General domain adaptation assume that the source and the target domain have the same label space.
Ningyu He, Jie Zhu
doaj +1 more source
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval [PDF]
North American Chapter of the Association for Computational Linguistics, 2021 Dense retrieval approaches can overcome the lexical gap and lead to significantly improved search results. However, they require large amounts of training data which is not available for most domains. As shown in previous work (Thakur et al., 2021b), the
Kexin Wang +3 more
semanticscholar +1 more source
Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder
Applied Sciences, 2022 Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature ...
Yi Zhu, Xinke Zhou, Xindong Wu
doaj +1 more source