  Continual learning and few-shot learning are important frontiers in progress
toward broader Machine Learning (ML) capabilities. Recently, there has been
intense interest in combining both. One of the first examples to do so was the
Continual few-shot Learning (CFSL) framework of Antoniou et al.
arXiv:2004.11967. In this study, we extend CFSL in two ways that capture a
broader range of challenges, important for intelligent agent behaviour in
real-world conditions. First, we increased the number of classes by an order of
magnitude, making the results more comparable to standard continual learning
experiments. Second, we introduced an 'instance test' which requires
recognition of specific instances of classes -- a capability of animal
cognition that is usually neglected in ML. For an initial exploration of ML
model performance under these conditions, we selected representative baseline
models from the original CFSL work and added a model variant with replay. As
expected, learning more classes is more difficult than the original CFSL
experiments, and interestingly, the way in which image instances and classes
are presented affects classification performance. Surprisingly, accuracy in the
baseline instance test is comparable to other classification tasks, but poor
given significant occlusion and noise. The use of replay for consolidation
substantially improves performance for both types of tasks, but particularly
for the instance test.
