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Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.
Least squares quantization in pcm
S. Lloyd · 1982
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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D. Ha, A. Dai, and Q. V. Le · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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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 · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil · 2018
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Unsupervised learning via meta-learning
K. Hsu, S. Levine, and C. Finn · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
B. Oreshkin, P. R. López, and A. Lacoste · 2018
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Low-shot learning with imprinted weights
H. Qi, M. Brown, and D. G. Lowe · 2018
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. H. Torr, and A. Vedaldi · 2019
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Meta-learning with differentiable convex optimization
K. Lee, S. Maji, A. Ravichandran, and S. Soatto · 2019
Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, and H. Larochelle · 2019
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The advantage of conditional meta-learning for biased regularization and fine-tuning
G. Denevi, M. Pontil, and C. Ciliberto · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Charting the right manifold: Manifold mixup for few-shot learning
P. Mangla, N. Kumari, A. Sinha, M. Singh, B. Krishnamurthy, and V. N. Balasubramanian · 2020
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Embedding propagation: Smoother manifold for few-shot classification
P. Rodríguez, I. Laradji, A. Drouin, and A. Lacoste · 2020
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Cited alongside, same era.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals · 2019
Cited alongside, same era.
Few-shot learning with embedded class models and shot-free meta training
A. Ravichandran, R. Bhotika, and S. Soatto · 2019
Cited alongside, same era.
Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2019
Cited alongside, same era.
Y. Tian, D. Krishnan, and P. Isola · 2019
Cited alongside, same era.
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola · 2020
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Structured prediction for conditional meta-learning
R. Wang, Y. Demiris, and C. Ciliberto · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
C. Zhang, Y. Cai, G. Lin, and C. Shen · 2020
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Few-shot classification with feature map reconstruction networks
D. Wertheimer, L. Tang, and B. Hariharan · 2021
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