A meta-learning method learns a neural-network distance-generating function for mirror descent, matching or beating preconditioned baselines on few-shot image classification while providing an O(1/epsilon^2) convergence rate.
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Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning
A meta-learning method learns a neural-network distance-generating function for mirror descent, matching or beating preconditioned baselines on few-shot image classification while providing an O(1/epsilon^2) convergence rate.