Multi source domain adaptation is an important technology in transfer learning,with the goal of improving the learning performance of the target domain by utilizing knowledge from multiple source domains.However,current multi-source domain adaptation methods mostly focus on the differences between the source domain and the target domain,ne-glecting the selection of the source domain.To address the above issues,this paper proposes a multi source domain adaptive algorithm based on multi-objective optimization,which enhances the effects of dissimilar domains between source domains and similar domains between source and target domains.
deep learningmulti source domain adaptationparticle swarm optimizationmulti-objective optimization
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