• Corpus ID: 309759

Prototypical Networks for Few-shot Learning

@inproceedings{Snell2017PrototypicalNF,
  title={Prototypical Networks for Few-shot Learning},
  author={Jake Snell and Kevin Swersky and Richard S. Zemel},
  booktitle={NIPS},
  year={2017}
}
A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. [...] Key Method Our method is competitive with state-of-the-art one-shot classification approaches while being much simpler and more scalable with the size of the support set. We empirically demonstrate the performance of our approach on the Omniglot and mini-ImageNet datasets. We further demonstrate that a similar idea can be used for zero…Expand
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Prototypical Siamese Networks for Few-shot Learning
  • Junhua Wang, Yongping Zhai
  • Computer Science
    2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC)
  • 2020
We propose a novel architecture, called Prototypical Siamese Networks, for few-shot learning, where a classifier must generalize to new classes not seen in the training set, given only a few examples…
Transductive Prototypical Network For Few-Shot Classification
TLDR
This paper proposes Transductive Prototypical Network (Td-PN), a universal transductive approach that refines the class representations by merging scarce labeled samples and high-confidence ones of target set in a classifying-friendly embedding space.
Learning to Compare: Relation Network for Few-Shot Learning
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A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.
Meta-Relation Networks for Few Shot Learning
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A meta-relation network is proposed to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each, based on relation networks and Model-Agnostic Meta-Learning training methods.
Subspace Networks for Few-shot Classification
We propose subspace 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 number of examples of each…
One-Way Prototypical Networks
TLDR
A new way of training prototypical few-shot models for just a single class is shown, and a novel Gaussian layer for distance calculation in a prototypical network is proposed, which takes the support examples' distribution rather than just their centroid into account.
Prototypical Bregman Networks
In this work, we approach one-shot and few-shot learning problems as methods for finding good prototypes for each class, where these prototypes are generalizable to new data samples and classes. We…
Meta-Learning for Semi-Supervised Few-Shot Classification
TLDR
This work proposes novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes, and confirms that these models can learn to improve their predictions due to unlabeling examples, much like a semi-supervised algorithm would.
Simultaneous Perturbation Stochastic Approximation for Few-Shot Learning
TLDR
This paper suggests to consider the new multi-task loss function and proposes the SPSA-like few-shot learning approach based on the prototypical networks method, and provides a theoretical justification and an analysis of experiments for this approach.
Semi-Supervised and Active Few-Shot Learning with Prototypical Networks
We consider the problem of semi-supervised few-shot classification where a classifier needs to adapt to new tasks using a few labeled examples and (potentially many) unlabeled examples. We propose a…
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