Semi-Supervised Domain Adaptation Using Explicit Class-Wise Matching for Domain-Invariant and Class-Discriminative Feature Learning

Semi-Supervised Domain Adaptation Using Explicit Class-Wise Matching for Domain-Invariant and Class-Discriminative Feature Learning

Semi-supervised domain adaptation (SSDA) is a promising technique for various applications. It can transfer knowledge learned from a source domain having high-density labeled samples to a target domain having limited labeled samples. Several previous works have attempted to reduce the distribution d...

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Bibliographic Details
Main Authors: Ba Hung Ngo, Jae Hyeon Park, So Jeong Park, Sung In Cho
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Semi-supervised learning
domain adaptation
classification
transfer learning
mapping function
Online Access:https://ieeexplore.ieee.org/document/9530420/

Internet

https://ieeexplore.ieee.org/document/9530420/

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