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- Дата: 3-06-2022, 14:58
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Название: Visual Domain Adaptation in the Deep Learning Era
Автор: Gabriela Csurka, Timothy M. Hospedales, Mathieu Salzmann, Tatiana Tommasi
Издательство: Morgan & Claypool
Год: 2022
Страниц: 190
Размер: 10.82 МБ
Формат: PDF
Язык: English
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains.