Unsupervised Domain Adaptation: Recent Advances and Future Perspectives

Unsupervised Domain Adaptation: Recent Advances and Future Perspectives Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. Autor: Jingjing Li, Lei Zhu Wydawnictwo: Springer Rok wydania: 2025 Okładka: miękka Liczba stron: 223 Wymiary: 23.5 x 15.5 cm Ilustracje: 44 Illustrations, color; 34 Illustrations, black and white Język: angielski ISBN: 9789819710270