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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… Expand
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Papers overview

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Highly Cited
2018
Highly Cited
2018
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces… Expand
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Highly Cited
2017
Highly Cited
2017
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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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… Expand
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