Self-adaptive label filtering learning for unsupervised domain adaptation

<正>1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation (UDA) aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source domain.Most of the existing UDA methods [2] align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating performance.