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We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free).
Long short-term memory
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Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Normface: L2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille · 2017
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Luca Bertinetto, João F. Henriques, Philip H. S. Torr, and Andrea Vedaldi · 2018
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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Improved few-shot learning with task conditioning and metric scaling
Boris N. Oreshkin, Pau Rodríguez, and Alexandre Lacoste · 2018
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
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Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
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Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, and Timothy M. Hospedales · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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