Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer

Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data. However, source data is not always accessible in practical scenarios, which restricts the application of SSDA in...

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Veröffentlicht in:	arXiv.org 2021-07
Hauptverfasser:	Wang, Xiaodong, Zhuo, Junbao, Cui, Shuhao, Wang, Shuhui
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation Consistency Domains Hypotheses Learning Regularization
Online-Zugang:	Volltext
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