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On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
[article]
2021
arXiv
pre-print
Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization} of model parameters (that we call meta-model) to rapidly adapt to new tasks using a small amount of labeled training data. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. In addition to generalization, robustness is also
arXiv:2102.10454v1
fatcat:itferwngljd73fuxkij7xuczbe