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The key issue of few-shot learning is learning to generalize.
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Large margin methods for structured and interdependent output variables
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Kilian Q. Weinberger, John Blitzer, and Lawrence K. Saul · 2005
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Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Large margin multi-task metric learning
Shibin Parameswaran and Kilian Q. Weinberger · 2010
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One shot learning of simple visual concepts
Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum · 2011
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Dan Zhang, Jingrui He, Yan Liu, Luo Si, and Richard D. Lawrence · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
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Deep learning face representation by joint identification-verification
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 2014
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Transductive multi-view zero-shot learning
Yanwei Fu, Timothy M. Hospedales, Tao Xiang, and Shaogang Gong · 2015
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Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Xin Li and Yuhong Guo · 2015
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Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Learning feed-forward one-shot learners
Luca Bertinetto, João F. Henriques, Jack Valmadre, and Philip H. S. Torrand Andrea Vedaldi · 2016
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Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Generative adversarial residual pairwise networks for one shot learning
Akshay Mehrotra and Ambedkar Dukkipati · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
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L2-constrained softmax loss for discriminative face verification
Rajeev Ranjan, Carlos D. Castillo, and Rama Chellappa · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Large-margin softmax loss for convolutional neural networks
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Gaussian prototypical networks for few-shot learning on omniglot
Stanislav Fort · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
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Normface: L2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan L. Yuille · 2017
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, and Stefanos Zafeiriou · 2018
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Few-shot learning with graph neural networks
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A simple neural attentive meta-learner
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Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravia, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 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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Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly
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