  Humans are capable of learning new concepts from only a few (labeled)
exemplars, incrementally and continually. This happens within the context that
we can differentiate among the exemplars, and between the exemplars and large
amounts of other data (unlabeled and labeled). This suggests, in human
learning, supervised learning of concepts based on exemplars takes place within
the larger context of contrastive self-supervised learning (CSSL) based on
unlabeled and labeled data. We discuss extending CSSL (1) to be based mainly on
exemplars and only secondly on data augmentation, and (2) to apply to both
unlabeled data (a large amount is available in general) and labeled data (a few
exemplars can be obtained with valuable supervised knowledge). A major benefit
of the extensions is that exemplar-based CSSL, with supervised finetuning,
supports few-shot class incremental learning (CIL). Specifically, we discuss
exemplar-based CSSL including: nearest-neighbor CSSL, neighborhood CSSL with
supervised pretraining, and exemplar CSSL with supervised finetuning. We
further discuss using exemplar-based CSSL to facilitate few-shot learning and,
in particular, few-shot CIL.
