Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDDownload PDF

Sep 30, 2021 (edited Dec 10, 2021)NeurIPS 2021 Workshop MetaLearn PosterReaders: Everyone
  • Keywords: Meta Learning, Bilevel Optimization, MAML
  • TL;DR: We interpret Model-Agnostic Meta-Learning (MAML) as a bilevel optimization problem (BLO) and leverage the sign-based SGD (signSGD) as a lower-level optimizer of BLO to come up with a new computationally-efficient first-order algorithm for MAML.
  • Abstract: We propose a new computationally-efficient first-order algorithm for Model-Agnostic Meta-Learning (MAML). The key enabling technique is to interpret MAML as a bilevel optimization (BLO) problem and leverage the sign-based SGD (signSGD) as a lower-level optimizer of BLO. We show that MAML, through the lens of signSGD-oriented BLO, naturally yields an alternating optimization scheme that just requires first-order gradients of a learned meta-model. We term the resulting MAML algorithm Sign-MAML. Compared to the conventional first-order MAML (FO-MAML) algorithm, Sign-MAML is theoretically-grounded as it does not impose any assumption on the absence of second-order derivatives during meta training. In practice, we show that Sign-MAML outperforms FO-MAML in various few-shot image classification tasks, and compared to MAML, it achieves a much more graceful tradeoff between classification accuracy and computation efficiency.
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  • Poster Session Selection: Poster session #2 (16:50 UTC+1)
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