Results 31 to 40 of about 3,020,093 (290)
Boosting for Unsupervised Domain Adaptation [PDF]
, 2013 To cope with machine learning problems where the learner receives data from different source and target distributions, a new learning framework named domain adaptation DA has emerged, opening the door for designing theoretically well-founded algorithms.
Amaury Habrard +2 more
openaire +2 more sources
Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction [PDF]
, 2010 This paper demonstrates how unsupervised cross-lingual adaptation of HMM-based speech synthesis models may be performed without explicit knowledge of the adaptation data language.
Teemu Hirsimaki +5 more
core +2 more sources
Unsupervised Intralingual and Cross-Lingual Speaker Adaptation for HMM-Based Speech Synthesis Using Two-Pass Decision Tree Construction [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 ...
Matthew Gibson, William Byrne
core +2 more sources
Unsupervised Transductive Domain Adaptation
CoRR, 2016 Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution.
Ozan Sener +3 more
openaire +2 more sources
Unsupervised Domain Adaptation for SAR Target Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recent years have witnessed great progress in synthetic aperture radar (SAR) target detection methods based on deep learning. However, these methods generally assume the training data and test data obey the same distribution, which does not always hold ...
Yu Shi, Lan Du, Yuchen Guo
doaj +1 more source
Unsupervised Domain Adaptation by Backpropagation
CoRR, 2014 Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available.
Yaroslav Ganin, Victor S. Lempitsky
openaire +4 more sources
Cross-Domain Error Minimization for Unsupervised Domain Adaptation [PDF]
, 2021 Accepted by DASFAA ...
Yuntao Du 0001 +4 more
openaire +4 more sources
BOOSTED UNSUPERVISED MULTI-SOURCE SELECTION FOR DOMAIN ADAPTATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Supervised machine learning needs high quality, densely sampled and labelled training data. Transfer learning (TL) techniques have been devised to reduce this dependency by adapting classifiers trained on different, but related, (source) training data ...
K. Vogt +4 more
doaj +1 more source
Contrastive Adaptation Network for Unsupervised Domain Adaptation [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Accepted by CVPR ...
Guoliang Kang +3 more
openaire +4 more sources
A Survey of Unsupervised Deep Domain Adaptation [PDF]
ACM Transactions on Intelligent Systems and Technology, 2020 Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case.
Garrett Wilson, Diane J. Cook
openaire +3 more sources