Prototype-Guided Feature Learning for Unsupervised Domain Adaptation

•A prototype-guided domain-invariant feature representation method is proposed to avoid harmful knowledge transfer from source domain to the target domain.•A modified nearest class prototype (MNCP) method is proposed to predict the target sample in the target subspace, which can make better use of the structure information of the target domain.•A multi-stage adaptive label filtering method is proposed to iteratively optimize the model, which can alleviate the errors introduced by pseudo-labeling.•Extensive experiments demonstrate that our approach is competitive with the mainstream unsupervised domain adaptive approaches.