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.

我们提出了一种算法来解决持续学习（CL）场景中的无监督域适应（UDA）问题，主要目标是在连续到达新领域时通过更新基本模型来维持模型概括性，而后续任务中只能访问未标记的数据。我们的解决方案是通过稳定学习的内部分布来提高模型在新领域上的概括性，内部分布使用高斯混合模型（GMM）进行建模，并将新领域的内部学习分布与估计的 GMM 进行匹配来更新模型，同时利用经验重播来克服灾难性遗忘问题，并提供理论分析来解释我们算法的有效性，通过广泛的比较和分析实验证明我们的方法是有效的，我们在四个基准数据集上进行实验以证明我们的方法的有效性。