Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms

This paper integrates the theoretical assumptions ensuring efficient meta-learning in the form of regularization terms into several popular meta- learning algorithms for which it provides a large study of their behavior on classic few-shot classification benchmarks.