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SYSTEM AND METHOD FOR DOMAIN ADAPTATION USING MARGINALIZED STACKED DENOISING AUTOENCODERS WITH DOMAIN PREDICTION REGULARIZATION
SYSTEM AND METHOD FOR DOMAIN ADAPTATION USING MARGINALIZED STACKED DENOISING AUTOENCODERS WITH DOMAIN PREDICTION REGULARIZATION
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机译：使用带域预测调节的边缘化分层去噪自动编码器进行域自适应的系统和方法
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摘要
A method for domain adaptation of samples includes receiving training samples from a plurality of domains, the plurality of domains including at least one source domain and a target domain, each training sample including values for a set of features. A domain predictor is learned on at least some of the training samples from the plurality of domains and respective domain labels. Domain adaptation is performed on the training samples using marginalized denoising autoencoding. This generates a domain adaptation transform layer (or layers) that transforms the training samples to a common adapted feature space. The domain adaptation employs the domain predictor to bias the domain adaptation towards one of the plurality of domains. Domain adapted training samples and their class labels can be used to train a classifier for prediction of class labels for unlabeled target samples that have been domain adapted with the domain adaptation transform layer(s).
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