  Few Shot Class Incremental Learning (FSCIL) with few examples per class for
each incremental session is the realistic setting of continual learning since
obtaining large number of annotated samples is not feasible and cost effective.
We present the framework MASIL as a step towards learning the maximal separable
classifier. It addresses the common problem i.e forgetting of old classes and
over-fitting to novel classes by learning the classifier weights to be
maximally separable between classes forming a simplex Equiangular Tight Frame.
We propose the idea of concept factorization explaining the collapsed features
for base session classes in terms of concept basis and use these to induce
classifier simplex for few shot classes. We further adds fine tuning to reduce
any error occurred during factorization and train the classifier jointly on
base and novel classes without retaining any base class samples in memory.
Experimental results on miniImageNet, CIFAR-100 and CUB-200 demonstrate that
MASIL outperforms all the benchmarks.
