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Matching Networks for One Shot Learning
Oriol Vinyals Charles Blundell Timothy Lillicrap
Google DeepMind Google DeepMind Google DeepMind
vinyals@ cblundell@ countzero@
6 Koray Kavukcuoglu Daan Wierstra
1 Google DeepMind Google DeepMind
0 korayk@ wierstra@
2
n
u Abstract
J
3
1 Learning from a few examples remains a key challenge in machine learning.
Despite recent advances in important domains such as vision and language, the
] standard supervised deep learning paradigm does not offer a satisfactory solution
G for learning new concepts rapidly from little data. In this work, we employ ideas
L from metric learning based on deep neural features and from recent advances
. that augment neural networks with external memories. Our framework learns a
s
c network that maps a small labelled support set and an unlabelled example to its
[ label, obviating the need for fine-tuning to adapt to new class types. We then define
one-shot learning problems on vision (using Omniglot, ImageNet) and language
1 tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to
v 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches.
0 We also demons
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