Multi Source Domain Adaptive Algorithm Based on Multi-objective Optimization
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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