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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.
Miller, Timothy A
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Unsupervised Domain Adaptation with Copula Models [PDF]
2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017 We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we ...
Pavlovic, Vladimir +2 more
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Unsupervised Domain Adaptation by Backpropagation [PDF]
, 2015 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 ...
Ganin, Yaroslav, Lempitsky, Victor
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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, Guangquan Zhang, Jie Lu
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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. Using the labelled data from the source domain, we first learn a projection that maximises the distance among ...
Cui, Xia, Bollegala, Danushka
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ACDC: Online unsupervised cross-domain adaptation
Knowledge-Based Systems, 2022 We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces -- a fully labeled source stream and an unlabeled target stream -- are learned together. Unique characteristics and challenges such as covariate shift, asynchronous concept drifts, and contrasting data ...
Marcus de Carvalho +3 more
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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. While some approaches to the problem require labeled data from the target domain, others adopt an unsupervised ...
Varsavsky, Thomas +5 more
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Benchmarking Domain Adaptation Methods on Aerial Datasets
Sensors, 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 ...
Navya Nagananda +6 more
doaj +1 more source
Multi-Step Online Unsupervised Domain Adaptation [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative ...
Moon, J. H. +2 more
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Inferring Latent Domains for Unsupervised Deep Domain Adaptation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data in a source domain. Most deep UDA approaches operate in a single-source, single-target scenario, i.e.
Massimiliano Mancini +4 more
openaire +6 more sources