Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation

Cited by: 0|Bibtex|Views8
Other Links: arxiv.org

Abstract:

Recent domain adaptation methods have demonstrated impressive improvement on unsupervised domain adaptation problems. However, in the semi-supervised domain adaptation (SSDA) setting where the target domain has a few labeled instances available, these methods can fail to improve performance. Inspired by the effectiveness of pseudo-label...More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments