Joint Distribution Adaptation via Feature and Model Matching
نویسندگان
چکیده
منابع مشابه
Joint Hierarchical Domain Adaptation and Feature Learning
Complex visual data contain discriminative structures that are difficult to be fully captured by any single feature descriptor. While recent work in domain adaptation focuses on adapting a single hand-crafted feature, it is important to perform adaptation on a hierarchy of features to exploit the richness of visual data. We propose a novel framework for domain adaptation using a sparse and hier...
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Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since ...
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Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature lev...
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ژورنال
عنوان ژورنال: Scientia Iranica
سال: 2018
ISSN: 2345-3605
DOI: 10.24200/sci.2018.5487.1304