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pro vyhledávání: '"Chowdhury, Sadman"'
Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Source-free OSDA (SF-OSDA) techniques eliminate
Externí odkaz:
http://arxiv.org/abs/2312.03767
Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adapt
Externí odkaz:
http://arxiv.org/abs/2308.00956
Domain adaptation (DA) strives to mitigate the domain gap between the source domain where a model is trained, and the target domain where the model is deployed. When a deep learning model is deployed on an aerial platform, it may face gradually degra
Externí odkaz:
http://arxiv.org/abs/2308.00924
Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place. However, current DA methods assume that the entire target domain is available
Externí odkaz:
http://arxiv.org/abs/2103.11056
Autor:
Rajat Sahay, Georgi Thomas, Chowdhury Sadman Jahan, Mihir Manjrekar, Dan Popp, Andreas Savakis
Publikováno v:
Sensors, Vol 23, Iss 20, p 8409 (2023)
Unsupervised domain adaptation (UDA) aims to mitigate the performance drop due to the distribution shift between the training and testing datasets. UDA methods have achieved performance gains for models trained on a source domain with labeled data to
Externí odkaz:
https://doaj.org/article/8f375b8e724749b0a4340c417e2c6197
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Transactions on Artificial Intelligence. :1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b495d2617f4bb17a60edb64fa92932a7
https://doi.org/10.1109/tai.2022.3233791
https://doi.org/10.1109/tai.2022.3233791
Autor:
Chowdhury, Sadman, Burton, Christopher
Publikováno v:
In Journal of Psychosomatic Research July 2017 98:10-18
Autor:
Navya Nagananda, Abu Md Niamul Taufique, Raaga Madappa, Chowdhury Sadman Jahan, Breton Minnehan, Todd Rovito, Andreas Savakis
Publikováno v:
Sensors, Vol 21, Iss 23, p 8070 (2021)
Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same under
Externí odkaz:
https://doaj.org/article/b29666a701c84c0fa8eb796715e60213