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Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.
Wordnet: a lexical database for english
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F.-F. Li · 2015
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Discriminative k-shot learning using probabilistic models
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The devil is in the tails: Fine-grained classification in the wild
G. V. Horn and P. Perona · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Meta-sgd: Learning to learn quickly for few shot learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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Few-shot learning through an information retrieval lens
E. Triantafillou, R. Zemel, and R. Urtasun · 2017
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Probabilistic model-agnostic meta-learning
C. Finn, K. Xu, and S. Levine · 2018
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Few-shot learning with graph neural networks
V. Garcia and J. Bruna · 2018
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Few-shot image recognition by predicting parameters from activations
S. Qiao, C. Liu, W. Shen, and A. L. Yuille · 2018
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Meta-learning for semi-supervised few-shot classification
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel · 2018
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
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The inaturalist species classification and detection dataset
G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2018
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Learning embedding adaptation for few-shot learning
H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha · 2018
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Dynamic few-shot visual learning without forgetting
S. Gidaris and N. Komodakis · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Y. Lee and S. Choi · 2018
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A simple neural attentive meta-learner
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel · 2018
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Rapid adaptation with conditionally shifted neurons
T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler · 2018
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On first-order meta-learning algorithms
A. Nichol, J. Achiam, and J. Schulman · 2018
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J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn · 2018
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A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
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Meta-learning probabilistic inference for prediction
J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner · 2019
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Learning to learn with conditional class dependencies
X. Jiang, M. Havaei, F. Varno, G. Chartrand, N. Chapados, and S. Matwin · 2019
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Learning to propagate labels: Transductive propagation network for few-shot learning
Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. J. Hwang, and Y. Yang · 2019
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Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2019
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Few-shot learning with localization in realistic settings
D. Wertheimer and B. Hariharan · 2019
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