Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation

Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains,...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 27; no. 9; pp. 4260 - 4273
Main Authors: Li, Shuang, Song, Shiji, Huang, Gao, Ding, Zhengming, Wu, Cheng
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation
Adaptation models
Classification
Data models
Domain adaptation
Feature extraction
Invariants
Learning systems
Measurement
Regression models
Representations
subspace learning
Visual discrimination
Visual tasks
Visualization
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains, and most existing approaches have concentrated on it. However, they often do not directly constrain the learned features to be class discriminative for both source and target data, which is of vital importance for the final classification. Therefore, in this paper, we put forward a novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD. Specifically, DICD is to learn a latent feature space with important data properties preserved, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter as much as possible. Experiments in this paper have demonstrated that the class discriminative properties will dramatically alleviate the cross-domain distribution inconsistency, which further boosts the classification performance. Moreover, we show that exploring both domain invariance and class discriminativeness of the learned representations can be integrated into one optimization framework, and the optimal solution can be derived effectively by solving a generalized eigen-decomposition problem. Comprehensive experiments on several visual cross-domain classification tasks verify that DICD can outperform the competitors significantly.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2018.2839528