Meta-Learning Update Rules for Unsupervised Representation Learning

ICLR 2019 Luke MetzNiru MaheswaranathanBrian CheungJascha Sohl-Dickstein

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect... (read more)

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