  Incremental learning attempts to develop a classifier which learns
continuously from a stream of data segregated into different classes. Deep
learning approaches suffer from catastrophic forgetting when learning classes
incrementally, while most incremental learning approaches require a large
amount of training data per class. We examine the problem of incremental
learning using only a few training examples, referred to as Few-Shot
Incremental Learning (FSIL). To solve this problem, we propose a novel approach
inspired by the concept learning model of the hippocampus and the neocortex
that represents each image class as centroids and does not suffer from
catastrophic forgetting. We evaluate our approach on three class-incremental
learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental
and few-shot incremental learning and show that our approach achieves
state-of-the-art results in terms of classification accuracy over all learned
classes.
