Bayesian Online Meta-Learning with Structured Laplace Approximation

  • Yap, Pau Ching*; Ritter, Hippolyt; Barber, David
  • Accepted abstract
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While there have been numerous attempts to overcome catastrophic forgetting for large-scale supervised classification, little has been done to overcome catastrophic forgetting for few-shot classification problems. We demonstrate that the popular gradient-based few-shot meta-learning algorithm model-agnostic meta-learning (MAML) indeed suffers from catastrophic forgetting and introduce a Bayesian online meta-learning framework that tackles this problem. Our framework incorporates MAML into a Bayesian online learning algorithm with Laplace approximation. The experimental evaluations demonstrate that our framework can effectively prevent forgetting in various few-shot classification settings compared to applying MAML sequentially.

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