Results 11 to 20 of about 3,020,093 (290)
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 +8 more sources
Unsupervised domain adaptation by domain invariant projection [PDF]
2013 IEEE International Conference on Computer Vision, 2013 Domain-invariant representations are key to addressing the domain shift problem where the training and test exam-ples follow different distributions.
Mahsa Baktashmotlagh +8 more
core +5 more sources
CUDA: Contradistinguisher for Unsupervised Domain Adaptation [PDF]
2019 IEEE International Conference on Data Mining (ICDM), 2019 Humans are very sophisticated in learning new information on a completely unknown domain because humans can contradistinguish, i.e., distinguish by contrasting qualities. We learn on a new unknown domain by jointly using unsupervised information directly
Dukkipati, Ambedkar +5 more
core +4 more sources
Self-adaptation for unsupervised domain adaptation [PDF]
Proceedings - Natural Language Processing in a Deep Learning World, 2019 Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches.
Bollegala, D +3 more
core +4 more sources
Simplified Neural Unsupervised Domain Adaptation [PDF]
Proceedings of the 2019 Conference of the North, 2019 Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain.
Timothy Miller, Miller, Timothy A
core +6 more sources
Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models [PDF]
, 2010 Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset ...
core +9 more sources
Unsupervised Domain Adaptation with Adapter
CoRR, 2021 Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from various domains. However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to ...
Rongsheng Zhang +3 more
openaire +3 more sources
Structure-Preserved Unsupervised Domain Adaptation
IEEE Transactions on Knowledge and Data Engineering, 2019 Domain adaptation has been a primal approach to addressing the issues by lack of labels in many data mining tasks. Although considerable efforts have been devoted to domain adaptation with promising results, most existing work learns a classifier on a ...
Hongfu Liu +7 more
core +3 more sources
Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images
Remote Sensing, 2022 Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and ...
Junbo Zhang +5 more
doaj +1 more source
Heterogeneous Domain Adaptation: An Unsupervised Approach [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Domain adaptation leverages the knowledge in one domain - the source domain - to improve learning efficiency in another domain - the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed, but only in situations where the target domain contains at least a few labeled instances.
Feng Liu 0003 +2 more
openaire +4 more sources