Exploring and reconstructing latent domains for multi-source domain adaptation

Multi-Source Domain Adaptation (MSDA) is receiving increased focus as a technique for reliably transferring knowledge from several source domains to a specific target domain. Traditional approaches generally operate under the assumption that samples in each source conform to a consistent distribution. However, real-world conditions often involve samples derived from diverse distributions, as well as potential data imbalance among different domains. Addressing these challenges, we introduce a novel and trustworthy framework, Multi-source Reconstructed Domain Adaptation (MSRDA), designed to enhance adaptation efficacy while maintaining robust performance and reliability across heterogeneous data sources. To start with,we delve into the latent mixed distributions of each source using clustering techniques, followed by the reconstruction of the latent domains following the original distribution. Additionally, we introduce an adaptive weighting mechanism to mitigate data imbalances.In cases where an latent domain consists of only a few samples, the global features are identified and dominate in that particular domain to help avoid overfitting. Moreover, given the difficulties of optimizing clustering while updating the model,we apply ExpectationMaximization (EM) algorithm to iteratively perform domain reconstruction and domain adaptation.Experiments are performed on two public datasets and one real-world datasets, and experimental results demonstrate that our MSRDA can effectively achieve multi-source domain adaptation through re-. constructing source domain with identified latent domains.