Unsupervised Domain Adaptation Using Compact Internal Representations
Mohammad Rostami
A major technique for tackling unsupervised domain adaptation involves
mapping data points from both the source and target domains into a shared
embedding space. The mapping encoder to the embedding space is trained such
that the embedding space becomes domain agnostic, allowing a classifier trained
on the source domain to generalize well on the target domain. To further
enhance the performance of unsupervised domain adaptation (UDA), we develop an
additional technique which makes the internal distribution of the source domain
more compact, thereby improving the model's ability to generalize in the target
domain.We demonstrate that by increasing the margins between data
representations for different classes in the embedding space, we can improve
the model performance for UDA. To make the internal representation more
compact, we estimate the internally learned multi-modal distribution of the
source domain as Gaussian mixture model (GMM). Utilizing the estimated GMM, we
enhance the separation between different classes in the source domain, thereby
mitigating the effects of domain shift. We offer theoretical analysis to
support outperofrmance of our method. To evaluate the effectiveness of our
approach, we conduct experiments on widely used UDA benchmark UDA datasets. The
results indicate that our method enhances model generalizability and
outperforms existing techniques.