  Both few-shot and continual learning have seen substantial progress in the
last years due to the introduction of proper benchmarks. That being said, the
field has still to frame a suite of benchmarks for the highly desirable setting
of continual few-shot learning, where the learner is presented a number of
few-shot tasks, one after the other, and then asked to perform well on a
validation set stemming from all previously seen tasks. Continual few-shot
learning has a small computational footprint and is thus an excellent setting
for efficient investigation and experimentation. In this paper we first define
a theoretical framework for continual few-shot learning, taking into account
recent literature, then we propose a range of flexible benchmarks that unify
the evaluation criteria and allows exploring the problem from multiple
perspectives. As part of the benchmark, we introduce a compact variant of
ImageNet, called SlimageNet64, which retains all original 1000 classes but only
contains 200 instances of each one (a total of 200K data-points) downscaled to
64 x 64 pixels. We provide baselines for the proposed benchmarks using a number
of popular few-shot learning algorithms, as a result, exposing previously
unknown strengths and weaknesses of those algorithms in continual and
data-limited settings.
