Abstract: As a new research direction in the field of machine learning,multi-source-free domain adaptation aims to transfer knowledge from multiple source domain models to the target domain,so as to achieve accurate prediction of target domain samples.Essentially,the key to solving multi-source-free domain adaptation lies in how to quantify the contribution of multiple source models to the target domain and utilize the diverse knowledge in the source models to adapt to the target domain.To address these issues,this paper proposes a multi-source-free domain adaptation method based on source model contribution quantization(SMCQ).Specifically,a source model transferability perception is proposed to quantify the transferability contribution of the source model,enabling the effective allocation of adaptive weights for target domain models.Additionally,an information maximization method is introduced to reduce cross-domain distributional discrepancies and mitigate model degradation.Subsequently,a credible partition global alignment approach is proposed to divide high-confidence and low-confidence samples to cope with the noisy environment caused by domain differences,effectively reduce the risk of incorrect label assignments.In addition,a sample local consistency loss is also introduced to mitigate the impact of pseudo-label noise on clustering errors of low-confidence samples.Finally,experiments conducted on multiple datasets validate the effectiveness of the proposed method.

Key words: Multi-source-free domain adaptation, Multi-model contribution quantization, Source-model transferable perception, Information maximum, Credible partition of samples