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Few-shot learning has become essential for producing models that generalize from few examples.
Acquiring a single new word
S. Carey and E. Bartlett · 1978
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Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement
J. Schmidhuber, J. Zhao, and M. Wiering · 1997
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Lifelong learning algorithms
S. Thrun · 1998
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Object classification from a single example utilizing class relevance metrics
M. Fink · 2005
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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One-shot learning of object categories
F.-F. Li, R. Fergus, and P. Perona · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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One-shot learning by inverting a compositional causal process
B. M. Lake, R. R. Salakhutdinov, and J. Tenenbaum · 2013
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Web-scale training for face identification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2015
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H. Edwards and A. Storkey · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
Cited alongside, same era.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Cited alongside, same era.
When is multitask learning effective? Semantic sequence prediction under varying data conditions
B. Plank and H. M. Alonso · 2017
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Attentive recurrent comparators
P. Shyam, S. Gupta, and A. Dukkipati · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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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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Discriminative k-shot learning using probabilistic models
M. Bauer, M. Rojas-Carulla, J. B. Świątkowski, B. Schölkopf, and R. E. Turner · 2017
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A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2017
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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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A. Lacoste, T. Boquet, N. Rostamzadeh, B. Oreshkin, W. Chung, and D. Krueger · 2017
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Learning visual reasoning without strong priors
E. Perez, H. de Vries, F. Strub, V. Dumoulin, and A. C. Courville · 2017
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Film: Visual reasoning with a general conditioning layer
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville · 2018
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Searching for activation functions
P. Ramachandran, B. Zoph, and Q. V. Lea · 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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Low-Shot Learning from Imaginary Data
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan · 2018
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