BOOSTED UNSUPERVISED MULTI-SOURCE SELECTION FOR DOMAIN ADAPTATION
نویسندگان
چکیده
منابع مشابه
Unsupervised Multi-Domain Adaptation with Feature Embeddings
Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches have two major weaknesses. First, they often require the specification of “pivot features” that generalize across domains, which are selected by taskspecific heuristics. We show that a novel but simple feature embedding approach provides better performance, by exploiting the feature tem...
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ژورنال
عنوان ژورنال: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2017
ISSN: 2194-9050
DOI: 10.5194/isprs-annals-iv-1-w1-229-2017