Unsupervised Learning via Meta-Learning

ICLR 2019 Kyle HsuSergey LevineChelsea Finn

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics... (read more)

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