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.
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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