  Although data-free incremental learning methods are memory-friendly,
accurately estimating and counteracting representation shifts is challenging in
the absence of historical data. This paper addresses this thorny problem by
proposing a novel incremental learning method inspired by human analogy
capabilities. Specifically, we design an analogy-making mechanism to remap the
new data into the old class by prompt tuning. It mimics the feature
distribution of the target old class on the old model using only samples of new
classes. The learnt prompts are further used to estimate and counteract the
representation shift caused by fine-tuning for the historical prototypes. The
proposed method sets up new state-of-the-art performance on four incremental
learning benchmarks under both the class and domain incremental learning
settings. It consistently outperforms data-replay methods by only saving
feature prototypes for each class. It has almost hit the empirical upper bound
by joint training on the Core50 benchmark. The code will be released at
\url{https://github.com/ZhihengCV/A-Prompts}.
