  We introduce an algorithm for tackling the problem of unsupervised domain
adaptation (UDA) in continual learning (CL) scenarios. The primary objective is
to maintain model generalization under domain shift when new domains arrive
continually through updating a base model when only unlabeled data is
accessible in subsequent tasks. While there are many existing UDA algorithms,
they typically require access to both the source and target domain datasets
simultaneously. Conversely, existing CL approaches can handle tasks that all
have labeled data. Our solution is based on stabilizing the learned internal
distribution to enhances the model generalization on new domains. The internal
distribution is modeled by network responses in hidden layer. We model this
internal distribution using a Gaussian mixture model (GMM ) and update the
model by matching the internally learned distribution of new domains to the
estimated GMM. Additionally, we leverage experience replay to overcome the
problem of catastrophic forgetting, where the model loses previously acquired
knowledge when learning new tasks. We offer theoretical analysis to explain why
our algorithm would work. We also offer extensive comparative and analytic
experiments to demonstrate that our method is effective. We perform experiments
on four benchmark datasets to demonstrate that our approach is effective.
