  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.
