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A major technique for tackling unsupervised domain adaptation involvesmapping data points from both the source and target domains into a sharedembedding space. The mapping encoder to the embedding space is trained suchthat the embedding space becomes domain agnostic, allowing a classifier trainedon the source domain to generalize well on the target domain. To furtherenhance the performance of unsupervised domain adaptation (UDA), we develop anadditional technique which makes the internal distribution of the source domainmore compact, thereby improving the model's ability to generalize in the targetdomain.We demonstrate that by increasing the margins between datarepresentations for different classes in the embedding space, we can improvethe model performance for UDA. To make the internal representation morecompact, we estimate the internally learned multi-modal distribution of thesource domain as Gaussian mixture model (GMM). Utilizing the estimated GMM, weenhance the separation between different classes in the source domain, therebymitigating the effects of domain shift. We offer theoretical analysis tosupport outperofrmance of our method. To evaluate the effectiveness of ourapproach, we conduct experiments on widely used UDA benchmark UDA datasets. Theresults indicate that our method enhances model generalizability andoutperforms existing techniques.

Unsupervised Domain Adaptation Using Compact Internal Representations