Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation

Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn a prediction model for the target domain given labeled source data and unlabeled target data. Impressive progress has been made by adversarial learning-based methods that align distributions across domains through deceiving a domain discriminator ne...

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Bibliographic Details
Main Authors: Xin Zhao, Shengsheng Wang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Domain adaptation
transfer learning
deep learning
image classification
Online Access:https://ieeexplore.ieee.org/document/8913529/

Internet

https://ieeexplore.ieee.org/document/8913529/

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