Meta-learning enables algorithms to quickly learn a newly encountered task
with just a few labeled examples by transferring previously learned knowledge.
However, the bottleneck of current meta-learning algorithms is the requirement
of a large number of meta-training tasks, which may not be accessible in
real-world scenarios. To address the challenge that available tasks may not
densely sample the space of tasks, we propose to augment the task set through
interpolation. By meta-learning with task interpolation (MLTI), our approach
effectively generates additional tasks by randomly sampling a pair of tasks and
interpolating the corresponding features and labels. Under both gradient-based
and metric-based meta-learning settings, our theoretical analysis shows MLTI
corresponds to a data-adaptive meta-regularization and further improves the
generalization. Empirically, in our experiments on eight datasets from diverse
domains including image recognition, pose prediction, molecule property
prediction, and medical image classification, we find that the proposed general
MLTI framework is compatible with representative meta-learning algorithms and
consistently outperforms other state-of-the-art strategies.

本文提出了一种通过任务插值来扩充任务集的元学习方法（MLTI），通过该方法可以实现更好的泛化性能，从而在多个数据集上显著优于当前最先进的策略。