  Class-Incremental Learning (CIL) trains a model to continually recognize new
classes from non-stationary data while retaining learned knowledge. A major
challenge of CIL arises when applying to real-world data characterized by
non-uniform distribution, which introduces a dual imbalance problem involving
(i) disparities between stored exemplars of old tasks and new class data
(inter-phase imbalance), and (ii) severe class imbalances within each
individual task (intra-phase imbalance). We show that this dual imbalance issue
causes skewed gradient updates with biased weights in FC layers, thus inducing
over/under-fitting and catastrophic forgetting in CIL. Our method addresses it
by reweighting the gradients towards balanced optimization and unbiased
classifier learning. Additionally, we observe imbalanced forgetting where
paradoxically the instance-rich classes suffer higher performance degradation
during CIL due to a larger amount of training data becoming unavailable in
subsequent learning phases. To tackle this, we further introduce a
distribution-aware knowledge distillation loss to mitigate forgetting by
aligning output logits proportionally with the distribution of lost training
data. We validate our method on CIFAR-100, ImageNetSubset, and Food101 across
various evaluation protocols and demonstrate consistent improvements compared
to existing works, showing great potential to apply CIL in real-world scenarios
with enhanced robustness and effectiveness.
