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Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
A cluster separation measure
David L Davies and Donald W Bouldin · 1979
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Wordnet: a lexical database for english
George A Miller · 1995
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A survey on transfer learning
Sinno Jialin Pan, Qiang Yang, et al · 2010
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One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2012
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep transfer metric learning
Junlin Hu, Jiwen Lu, and Yap-Peng Tan · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 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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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
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Emnist: an extension of mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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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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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross Girshick · 2017
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Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Few-shot learning with metric-agnostic conditional embeddings
Nathan Hilliard, Lawrence Phillips, Scott Howland, Artëm Yankov, Courtney D Corley, and Nathan O Hodas · 2018
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Learning to cluster in order to transfer across domains and tasks
Yen-Chang Hsu, Zhaoyang Lv, and Zsolt Kira · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Low-shot learning with imprinted weights
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2018
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Domain adaption in one-shot learning
Nanqing Dong and Eric P Xing · 2018
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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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Hang Qi, Matthew Brown, and David G Lowe · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Low-shot learning from imaginary data
Yu-Xiong Wang, Ross Girshick, Martial Hebert, and Bharath Hariharan · 2018
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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, João F Henriques, Philip HS Torr, and Andrea Vedaldi · 2019
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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 · 2019
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