  Few-shot class-incremental learning (FSCIL) aims to learn progressively about
new classes with very few labeled samples, without forgetting the knowledge of
already learnt classes. FSCIL suffers from two major challenges: (i)
over-fitting on the new classes due to limited amount of data, (ii)
catastrophically forgetting about the old classes due to unavailability of data
from these classes in the incremental stages. In this work, we propose a
self-supervised stochastic classifier (S3C) to counter both these challenges in
FSCIL. The stochasticity of the classifier weights (or class prototypes) not
only mitigates the adverse effect of absence of large number of samples of the
new classes, but also the absence of samples from previously learnt classes
during the incremental steps. This is complemented by the self-supervision
component, which helps to learn features from the base classes which generalize
well to unseen classes that are encountered in future, thus reducing
catastrophic forgetting. Extensive evaluation on three benchmark datasets using
multiple evaluation metrics show the effectiveness of the proposed framework.
We also experiment on two additional realistic scenarios of FSCIL, namely where
the number of annotated data available for each of the new classes can be
different, and also where the number of base classes is much lesser, and show
that the proposed S3C performs significantly better than the state-of-the-art
for all these challenging scenarios.
