  Current research on continual learning mainly focuses on relieving
catastrophic forgetting, and most of their success is at the cost of limiting
the performance of newly incoming tasks. Such a trade-off is referred to as the
stability-plasticity dilemma and is a more general and challenging problem for
continual learning. However, the inherent conflict between these two concepts
makes it seemingly impossible to devise a satisfactory solution to both of them
simultaneously. Therefore, we ask, "is it possible to divide them into two
separate problems to conquer them independently?". To this end, we propose a
prompt-tuning-based method termed PromptFusion to enable the decoupling of
stability and plasticity. Specifically, PromptFusion consists of a carefully
designed \stab module that deals with catastrophic forgetting and a \boo module
to learn new knowledge concurrently. Furthermore, to address the computational
overhead brought by the additional architecture, we propose PromptFusion-Lite
which improves PromptFusion by dynamically determining whether to activate both
modules for each input image. Extensive experiments show that both PromptFusion
and PromptFusion-Lite achieve promising results on popular continual learning
datasets for class-incremental and domain-incremental settings. Especially on
Split-Imagenet-R, one of the most challenging datasets for class-incremental
learning, our method can exceed state-of-the-art prompt-based methods by more
than 5\% in accuracy, with PromptFusion-Lite using 14.8\% less computational
resources than PromptFusion.
