  The ability to learn new concepts with small amounts of data is a critical
aspect of intelligence that has proven challenging for deep learning methods.
Meta-learning has emerged as a promising technique for leveraging data from
previous tasks to enable efficient learning of new tasks. However, most
meta-learning algorithms implicitly require that the meta-training tasks be
mutually-exclusive, such that no single model can solve all of the tasks at
once. For example, when creating tasks for few-shot image classification, prior
work uses a per-task random assignment of image classes to N-way classification
labels. If this is not done, the meta-learner can ignore the task training data
and learn a single model that performs all of the meta-training tasks
zero-shot, but does not adapt effectively to new image classes. This
requirement means that the user must take great care in designing the tasks,
for example by shuffling labels or removing task identifying information from
the inputs. In some domains, this makes meta-learning entirely inapplicable. In
this paper, we address this challenge by designing a meta-regularization
objective using information theory that places precedence on data-driven
adaptation. This causes the meta-learner to decide what must be learned from
the task training data and what should be inferred from the task testing input.
By doing so, our algorithm can successfully use data from
non-mutually-exclusive tasks to efficiently adapt to novel tasks. We
demonstrate its applicability to both contextual and gradient-based
meta-learning algorithms, and apply it in practical settings where applying
standard meta-learning has been difficult. Our approach substantially
outperforms standard meta-learning algorithms in these settings.
