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Domain Neural Adaptation
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AI
DOI：10.1109/TNNLS.2022.3151683.png)
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摘要
En 中文
Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization. In this article, we introduce domain neural adaptation (DNA): an approach that exploits nonlinear deep neural network to 1) match the source and target joint distributions in the network activation space and 2) learn the classifier in an end-to-end manner. Specifically, we employ the relative chi-square divergence to compare the two joint distributions, and show that the divergence can be estimated via seeking the maximal value of a quadratic functional over the reproducing kernel hilbert space. The analytic solution to this maximization problem enables us to explicitly express the divergence estimate as a function of the neural network mapping. We optimize the network parameters to minimize the estimated joint distribution divergence and the classification loss, yielding a classification model that generalizes well to the target domain. Empirical results on several visual datasets demonstrate that our solution is statistically better than its competitors.
Keyword:
Adaptation models
Probability distribution
DNA
Neural networks
Kernel
Hilbert space
Data models
Domain adaptation
joint distribution matching
neural network
relative chi-square (RCS) divergence
reproducing kernel hilbert space (RKHS)
期刊
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8.9
论文数:
7.6K
被引数:
7.2W
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