Meta-learning with differentiable closed-form solvers
Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. None...
| Main Authors: | , , , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
OpenReview
2019
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