  Few-shot learning aims to transfer the knowledge acquired from training on a
diverse set of tasks to unseen tasks from the same task distribution with a
limited amount of labeled data. The underlying requirement for effective
few-shot generalization is to learn a good representation of the task manifold.
This becomes more difficult when only a limited number of tasks are available
for training. In such a few-task few-shot setting, it is beneficial to
explicitly preserve the local neighborhoods from the task manifold and exploit
this to generate artificial tasks for training. To this end, we introduce the
notion of interval bounds from the provably robust training literature to
few-shot learning. The interval bounds are used to characterize neighborhoods
around the training tasks. These neighborhoods can then be preserved by
minimizing the distance between a task and its respective bounds. We then use a
novel strategy to artificially form new tasks for training by interpolating
between the available tasks and their respective interval bounds. We apply our
framework to both model-agnostic meta-learning as well as prototype-based
metric-learning paradigms. The efficacy of our proposed approach is evident
from the improved performance on several datasets from diverse domains compared
to current methods.
