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Both few-shot and continual learning have seen substantial progress in thelast years due to the introduction of proper benchmarks. That being said, thefield has still to frame a suite of benchmarks for the highly desirable settingof continual few-shot learning, where the learner is presented a number offew-shot tasks, one after the other, and then asked to perform well on avalidation set stemming from all previously seen tasks. Continual few-shotlearning has a small computational footprint and is thus an excellent settingfor efficient investigation and experimentation. In this paper we first definea theoretical framework for continual few-shot learning, taking into accountrecent literature, then we propose a range of flexible benchmarks that unifythe evaluation criteria and allows exploring the problem from multipleperspectives. As part of the benchmark, we introduce a compact variant ofImageNet, called SlimageNet64, which retains all original 1000 classes but onlycontains 200 instances of each one (a total of 200K data-points) downscaled to64 x 64 pixels. We provide baselines for the proposed benchmarks using a numberof popular few-shot learning algorithms, as a result, exposing previouslyunknown strengths and weaknesses of those algorithms in continual anddata-limited settings.

Defining Benchmarks for Continual Few-Shot Learning