On Learning Invariant Representation for Domain Adaptation

27 Jan 2019Han ZhaoRemi Tachet des CombesKun ZhangGeoffrey J. Gordon

Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain... (read more)

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