  Model-agnostic meta-learning (MAML) is a popular method for few-shot learning
but assumes that we have access to the meta-training set. In practice, training
on the meta-training set may not always be an option due to data privacy
concerns, intellectual property issues, or merely lack of computing resources.
In this paper, we consider the novel problem of repurposing pretrained MAML
checkpoints to solve new few-shot classification tasks. Because of the
potential distribution mismatch, the original MAML steps may no longer be
optimal. Therefore we propose an alternative meta-testing procedure and combine
MAML gradient steps with adversarial training and uncertainty-based stepsize
adaptation. Our method outperforms "vanilla" MAML on same-domain and
cross-domains benchmarks using both SGD and Adam optimizers and shows improved
robustness to the choice of base stepsize.
