  The reason for Meta Overfitting can be attributed to two factors: Mutual
Non-exclusivity and the Lack of diversity, consequent to which a single global
function can fit the support set data of all the meta-training tasks and fail
to generalize to new unseen tasks. This issue is evidenced by low error rates
on the meta-training tasks, but high error rates on new tasks. However, there
can be a number of novel solutions to this problem keeping in mind any of the
two objectives to be attained, i.e. to increase diversity in the tasks and to
reduce the confidence of the model for some of the tasks. In light of the
above, this paper proposes a number of solutions to tackle meta-overfitting on
few-shot learning settings, such as few-shot sinusoid regression and few shot
classification. Our proposed approaches demonstrate improved generalization
performance compared to state-of-the-art baselines for learning in a
non-mutually exclusive task setting. Overall, this paper aims to provide
insights into tackling overfitting in meta-learning and to advance the field
towards more robust and generalizable models.
