Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks
作者： Ziliang Chen , Jingyu Zhuang , Xiaodan Liang , Liang Lin
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摘要: Abstract (Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains (Multi-target DA). In this paper, we consider a more realistic transfer scenario: our target domain is comprised of multiple sub-targets implicitly blended with each other so that learners could not identify …
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