Titlebook: Domain Adaptation in Computer Vision Applications; Gabriela Csurka Book 2017 Springer International Publishing AG 2017 Computer Vision.Vis

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Book 2017ccess to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic se

清洗

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Reem Ashour,Sara Aldhaheri,Yasmeen Abu-Kheil and in particular on the KL divergence and the Hellinger distance. Throughout the chapter, we evaluate the different methods and distance measures on the task of visual object recognition and compare them against related baselines on a standard DA benchmark dataset.

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Elvia Giovanna Battaglia,Elisabetta Romawed from the speech community. We explain under which conditions the domain influence is canceled out and show experimentally on two in-house license plate matching databases that the proposed approach improves accuracy.