Learning to Generalize to Unseen Tasks with Bilevel Optimization

5 Aug 2019Hayeon LeeDonghyun NaHae Beom LeeSung Ju Hwang

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 with random classes... (read more)

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