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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| Format: | Article |
| Language: | English |
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ICLR
2020
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| Online Access: | https://hdl.handle.net/1721.1/126609 |