  Few-shot class-incremental learning (FSCIL) has been proposed aiming to
enable a deep learning system to incrementally learn new classes with limited
data. Recently, a pioneer claims that the commonly used replay-based method in
class-incremental learning (CIL) is ineffective and thus not preferred for
FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In
this paper, we show through empirical results that adopting the data replay is
surprisingly favorable. However, storing and replaying old data can lead to a
privacy concern. To address this issue, we alternatively propose using
data-free replay that can synthesize data by a generator without accessing real
data. In observing the the effectiveness of uncertain data for knowledge
distillation, we impose entropy regularization in the generator training to
encourage more uncertain examples. Moreover, we propose to relabel the
generated data with one-hot-like labels. This modification allows the network
to learn by solely minimizing the cross-entropy loss, which mitigates the
problem of balancing different objectives in the conventional knowledge
distillation approach. Finally, we show extensive experimental results and
analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the
effectiveness of our proposed one.
