  Few-shot class-incremental learning (FSCIL) faces challenges of memorizing
old class distributions and estimating new class distributions given few
training samples. In this study, we propose a learnable distribution
calibration (LDC) approach, with the aim to systematically solve these two
challenges using a unified framework. LDC is built upon a parameterized
calibration unit (PCU), which initializes biased distributions for all classes
based on classifier vectors (memory-free) and a single covariance matrix. The
covariance matrix is shared by all classes, so that the memory costs are fixed.
During base training, PCU is endowed with the ability to calibrate biased
distributions by recurrently updating sampled features under the supervision of
real distributions. During incremental learning, PCU recovers distributions for
old classes to avoid `forgetting', as well as estimating distributions and
augmenting samples for new classes to alleviate `over-fitting' caused by the
biased distributions of few-shot samples. LDC is theoretically plausible by
formatting a variational inference procedure. It improves FSCIL's flexibility
as the training procedure requires no class similarity priori. Experiments on
CUB200, CIFAR100, and mini-ImageNet datasets show that LDC outperforms the
state-of-the-arts by 4.64%, 1.98%, and 3.97%, respectively. LDC's effectiveness
is also validated on few-shot learning scenarios.
