Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge sequentially and have continuously evolving distributions. Restoring and adapting to such target data results in escalating computational and resource consumption over time. Hence, it is vital to devise algorithms to address the evolving domain adaptation (EDA) problem, \emph{i.e.,} adapting models to evolving target domains without access to historic target domains. To achieve this goal, we propose a simple yet effective approach, termed progressive conservative adaptation (PCAda). To manage new target data that diverges from previous distributions, we fine-tune the classifier head based on the progressively updated class prototypes. Moreover, as adjusting to the most recent target domain can interfere with the features learned from previous target domains, we develop a conservative sparse attention mechanism. This mechanism restricts feature adaptation within essential dimensions, thus easing the inference related to historical knowledge. The proposed PCAda is implemented with a meta-learning framework, which achieves the fast adaptation of the classifier with the help of the progressively updated class prototypes in the inner loop and learns a generalized feature without severely interfering with the historic knowledge via the conservative sparse attention in the outer loop. Experiments on Rotated MNIST, Caltran, and Portraits datasets demonstrate the effectiveness of our method.

翻译：传统的领域适应通常将知识从源域迁移至静态目标域。然而现实场景中，目标数据往往以序列形式出现且分布持续演化。若对这类目标数据持续恢复与适配，计算和资源消耗将随时间呈指数级增长。因此，亟需设计算法解决演化域适应（EDA）问题，即在无法访问历史目标域的条件下，使模型适应持续演化的目标域。为此，我们提出一种简洁高效的方法——渐进式保守适应（PCAda）。针对偏离先前分布的新目标数据，我们基于渐进更新的类别原型微调分类器头部。此外，由于适配最新目标域可能干扰先前目标域习得的特征，我们设计了保守型稀疏注意力机制。该机制将特征适配约束在关键维度中，从而缓解历史知识相关的推断干扰。提出的PCAda采用元学习框架实现：在内循环中借助渐进更新的类别原型实现分类器快速适应，在外循环中通过保守型稀疏注意力在不过度干扰历史知识的前提下学习泛化特征。在Rotated MNIST、Caltran和Portraits数据集上的实验验证了本方法的有效性。