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Learning to Generalize to Unseen Tasks with Bilevel Optimization
[article]
2019
arXiv
pre-print
Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distribution, have been shown to be effective for few-shot classification tasks of unseen classes. They are often trained with episodic training where they iteratively train a common metric space that reduces distance between the class representatives and instances belonging to each class, over large number of episodes
arXiv:1908.01457v1
fatcat:h4pqh4lcgzg4bgbz7nvq3xfexe