  Non-exemplar class-incremental learning (NECIL) is to resist catastrophic
forgetting without saving old class samples. Prior methodologies generally
employ simple rules to generate features for replaying, suffering from large
distribution gap between replayed features and real ones. To address the
aforementioned issue, we propose a simple, yet effective
\textbf{Diff}usion-based \textbf{F}eature \textbf{R}eplay (\textbf{DiffFR})
method for NECIL. First, to alleviate the limited representational capacity
caused by fixing the feature extractor, we employ Siamese-based self-supervised
learning for initial generalizable features. Second, we devise diffusion models
to generate class-representative features highly similar to real features,
which provides an effective way for exemplar-free knowledge memorization.
Third, we introduce prototype calibration to direct the diffusion model's focus
towards learning the distribution shapes of features, rather than the entire
distribution. Extensive experiments on public datasets demonstrate significant
performance gains of our DiffFR, outperforming the state-of-the-art NECIL
methods by 3.0\% in average. The code will be made publicly available soon.
