Model Adaptation: Unsupervised Domain Adaptation Without Source Data

In this paper, we investigate a challenging unsupervised domain adaptation setting --- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be a...

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Bibliographic Details
Main Authors: Li, Rui, Jiao, Qianfen, Cao, Wenming, Wong, Hau-San, Wu, Si
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Data models
Gallium nitride
Generative adversarial networks
Generators
Predictive models
Training
Online Access:Request full text
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