The Transfer Learning via Selecting Confident Pseudo-Labels
Domain adaptation aims to transfer knowledge from well-labeled source domain to unlabeled target domain.Selective pseudo-labels and label propagation are common methods of domain adaptation.The existing methods have the following drawbacks.On the one hand,traditional selective pseudo-label classifies samples with the largest predicted probability of class and ignoring other probabilities.On the other hand,traditional label propa-gation equally treats labels with different confidence,which may lead to mislabeling.Therefore,the transfer learning via selecting confident pseudo-labels(TL-SCP)is proposed.Firstly,when evaluating confidence of pseudo-labels,the maximum prediction probability of the class and the prominence of other prediction probabilities are computed.Secondly,label propagation keeps high confidence labels and let them guide the update of low-confidence labels,so as to reduce the propagation of false label.Finally,a large number of experiments on four benchmark datasets vali-date the proposed model(TL-SCP)over existing advanced models.
NETL
NSTL
万方数据
