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Unsupervised Few-Shot Feature Learning via Self-Supervised Training
Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a mor...
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| Publicat a: | Front Comput Neurosci |
|---|---|
| Autors principals: | , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Frontiers Media S.A.
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7592391/ https://ncbi.nlm.nih.gov/pubmed/33178000 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fncom.2020.00083 |
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