Deep Triplet Ranking Networks for One-Shot Recognition

Meng Ye
Meng Ye

arXiv: Learning, Volume abs/1804.07275, 2018.

Cited by: 1|Bibtex|Views11
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme cas...More

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