Unsupervised Learning via Meta-Learning Unsupervised Learning via Meta-Learning
Paper summary What is stopping us from applying meta-learning to new tasks? Where do the tasks come from? Designing task distribution is laborious. We should automatically learn tasks! Unsupervised Learning via Meta-Learning: The idea is to use a distance metric in an out-of-the-box unsupervised embedding space created by BiGAN/ALI or DeepCluster to construct tasks in an unsupervised way. If you cluster points to randomly define classes (e.g. random k-means) you can then sample tasks of 2 or 3 classes and use them to train a model. Where does the extra information come from? The metric space used for k-means asserts specific distances. The intuition why this works is that it is useful model initialization for downstream tasks. This summary was written with the help of Chelsea Finn.
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Unsupervised Learning via Meta-Learning
Kyle Hsu and Sergey Levine and Chelsea Finn
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, cs.AI, cs.CV, stat.ML

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