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

MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning