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.

本文提出一种在没有访问元培训练集的情况下，利用预训练MAML检查点来解决新的few-shot分类任务的方法，通过结合对抗训练和基于不确定性的步长适应，以及对算法进行改进，在SGD和Adam优化器上，在同一领域和跨领域基准测试中优于“基准”MAML，并表现出对基础步长的选择具有更好的鲁棒性。

复用预训练模型进行鲁棒的领域外少样本学习