7.4k citations · 22.9k across the 86 of their papers we have counts for
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Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the inc…
Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang +1
Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to th…
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, Richard S. Zemel
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small numb…