Unsupervised Domain Adaptation by Mapped Correlation Alignment

Unsupervised Domain Adaptation by Mapped Correlation Alignment

The goal of unsupervised domain adaptation aims to utilize labeled data from source domain to annotate the target-domain data, which has none of the labels. Existing work uses Siamese network-based models to minimize the domain discrepancy to learn a domain-invariant feature. Alignment of the second...

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Bibliographic Details
Main Authors: Yun Zhang, Nianbin Wang, Shaobin Cai, Lei Song
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Transfer learning
domain adaptation
deep learning
image recognition
Online Access:https://ieeexplore.ieee.org/document/8434290/

Internet

https://ieeexplore.ieee.org/document/8434290/

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