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Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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One-shot learning of object categories
Fei-Fei Li, Robert Fergus, and Pietro Perona · 2006
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Semi-supervised learning
Chapelle Olivier, Schölkopf Bernhard, and Zien Alexander · 2006
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee Dong-Hyun · 2013
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Transfer learning in a transductive setting
Marcus Rohrbach, Sandra Ebert, and Bernt Schiele · 2013
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Isaac Triguero, Salvador García, and Francisco Herrera · 2015
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Deep learning
LeCun Yann, Bengio Yoshua, and Hinton Geoffrey · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Virtual adversarial training for semi-supervised text classification
Takeru Miyato, Andrew M. Dai, and Ian J. Goodfellow · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan 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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Fully convolutional networks for semantic segmentation
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Realistic evaluation of deep semi-supervised learning algorithms
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TADAM: task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodríguez, and Alexandre Lacoste · 2018
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Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
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Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogério Schmidt Feris, Abhishek Kumar, Raja Giryes, and Alexander M. Bronstein · 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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Probabilistic model-agnostic meta-learning
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross B. Girshick, Martial Hebert, and Bharath Hariharan · 2018
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How to train your maml
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Meta-transfer learning for few-shot learning
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Transductive propagation network for few-shot learning
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