Results 41 to 50 of about 82,326 (277)
Unsupervised Domain Adaptation on Reading Comprehension
, 2020 Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear.
Cao, Yu +3 more
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Augmentation based unsupervised domain adaptation [PDF]
, 2022 The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize.
Orbes-Arteaga, Mauricio +7 more
openaire +2 more sources
Privacy-Preserving Unsupervised Domain Adaptation in Federated Setting
IEEE Access, 2020 The training of deep neural networks relies on massive high-quality labeled data which is expensive in practice. To tackle this problem, domain adaptation is proposed to transfer knowledge from label-rich source domain to unlabeled target domain to learn
Lei Song +3 more
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Unsupervised Domain Adaptation for Multispectral Pedestrian Detection [PDF]
, 2019 Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance.
Cao, Yanlong +6 more
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IEEE Access, 2019 Deep convolutional networks have demonstrated state-of-the-art performance on various challenging medical image processing tasks. Leveraging images from different modalities for the same analysis task holds large clinical benefits.
Qi Dou +6 more
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Domain‐specific feature recalibration and alignment for multi‐source unsupervised domain adaptation
IET Computer Vision, 2023 Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this
Mengzhu Wang +6 more
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Unsupervised multi-target domain adaptation for deforestation detection in tropical rainforest [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesGeographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote ...
M. X. Ortega Adarme +5 more
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Frontiers in Neuroscience, 2023 The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors.
Chuanbo Qin +6 more
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Unsupervised Domain Adaptation with Similarity Learning
, 2018 The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation
Pinheiro, Pedro O.
core +1 more source
Return of Frustratingly Easy Domain Adaptation
, 2015 Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning ...
Feng, Jiashi, Saenko, Kate, Sun, Baochen
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