  Continual learning is a challenging problem in machine learning, especially
for image classification tasks with imbalanced datasets. It becomes even more
challenging when it involves learning new classes incrementally. One method for
incremental class learning, addressing dataset imbalance, is rehearsal using
previously stored data. In rehearsal-based methods, access to previous data is
required for either training the classifier or the generator, but it may not be
feasible due to storage, legal, or data access constraints. Although there are
many rehearsal-free alternatives for class incremental learning, such as
parameter or loss regularization, knowledge distillation, and dynamic
architectures, they do not consistently achieve good results, especially on
imbalanced data. This paper proposes a new approach called Data-Free Generative
Replay (DFGR) for class incremental learning, where the generator is trained
without access to real data. In addition, DFGR also addresses dataset imbalance
in continual learning of an image classifier. Instead of using training data,
DFGR trains a generator using mean and variance statistics of batch-norm and
feature maps derived from a pre-trained classification model. The results of
our experiments demonstrate that DFGR performs significantly better than other
data-free methods and reveal the performance impact of specific parameter
settings. DFGR achieves up to 88.5% and 46.6% accuracy on MNIST and
FashionMNIST datasets, respectively. Our code is available at
https://github.com/2younis/DFGR
