Meta-learning for semi-supervised few-shot classification

In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes repr...

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Bibliographic Details
Main Author: Tenenbaum, Joshua B (Author)
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: ICLR, 2020-08-17T11:52:33Z.
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Online Access:Get fulltext

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