  Continual Learning (CL) poses a significant challenge in Artificial
Intelligence, aiming to mirror the human ability to incrementally acquire
knowledge and skills. While extensive research has focused on CL within the
context of classification tasks, the advent of increasingly powerful generative
models necessitates the exploration of Continual Learning of Generative models
(CLoG). This paper advocates for shifting the research focus from
classification-based CL to CLoG. We systematically identify the unique
challenges presented by CLoG compared to traditional classification-based CL.
We adapt three types of existing CL methodologies, replay-based,
regularization-based, and parameter-isolation-based methods to generative tasks
and introduce comprehensive benchmarks for CLoG that feature great diversity
and broad task coverage. Our benchmarks and results yield intriguing insights
that can be valuable for developing future CLoG methods. Additionally, we will
release a codebase designed to facilitate easy benchmarking and experimentation
in CLoG publicly at https://github.com/linhaowei1/CLoG. We believe that
shifting the research focus to CLoG will benefit the continual learning
community and illuminate the path for next-generation AI-generated content
(AIGC) in a lifelong learning paradigm.
