Selecting Relevant Features from a Universal Representation for Few-shot Classification

Dvornik Nikita
Dvornik Nikita
Cited by: 0|Bibtex|Views58
Other Links: arxiv.org

Abstract:

Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than p...More

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