Báo cáo khoa học: "Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation"
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We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both domains and ensure that at least some of the latent variables are predictive of the label on the source domain.
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