Meta-Learning Update Rules for Unsupervised Representation Learning

Understand

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.
• In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks.
• Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.