Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning

By Momin Abbas, Quan-Wu Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen. 2022

108 citations recorded by the source corpus.

Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points and local minimizers than its empirical risk minimization counterpart. To address this challenge, we leverage the recently invented sharpness-aware minimization and develop a sharpness-aware MAML approach that we term Sharp-MAML. We empirically demonstrate that Sharp-MAML and its computation-efficient variant can outperform the plain-vanilla MAML baseline (e.g., $+3\%$ accuracy on Mini-Imagenet). We complement the empirical study with the convergence rate analysis and the generalization bound of Sharp-MAML. To the best of our knowledge, this is the first empirical and theoretical study on sharpness-aware minimization in the context of bilevel learning. The code is available at https://github.com/mominabbass/Sharp-MAML.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Towards Label-Efficient Incremental Learning: A Survey (2023, background): “One potential remedy would be to rely on Sharpness-Aware Minimization, as few-shot learning is shown to exhibit loss landscape with sharp and poor local minimum [ Abbas et al. , 2022 ] .”