Domain adaptation

Domain Adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data… (More)
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Topic mentions per year

1983-2017
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Highly Cited
2015
Highly Cited
2015
Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain… (More)
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Highly Cited
2013
Highly Cited
2013
In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by… (More)
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Highly Cited
2012
Highly Cited
2012
In real-world applications of visual recognition, many factors - such as pose, illumination, or image quality - can cause a… (More)
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Highly Cited
2011
Highly Cited
2011
Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that… (More)
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Highly Cited
2009
Highly Cited
2009
Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively… (More)
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Highly Cited
2007
Highly Cited
2007
We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do… (More)
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Highly Cited
2007
Highly Cited
2007
Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains… (More)
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Highly Cited
2006
Highly Cited
2006
Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution… (More)
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Highly Cited
2006
Highly Cited
2006
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same… (More)
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Highly Cited
2006
Highly Cited
2006
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and… (More)
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