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Learning with limited data is a key challenge for visual recognition.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
One-shot learning of object categories
F.-F. Li, R. Fergus, and P. Perona · 2006
Earlier work this paper cites.
One shot learning of simple visual concepts
B. M. Lake, R. Salakhutdinov, J. Gross, and J. B. Tenenbaum · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Label-embedding for attribute-based classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2013
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Earlier work this paper cites.
L. J. Ba, R. Kiros, and G. E. Hinton · 2016
Earlier work this paper cites.
Synthesized classifiers for zero-shot learning
S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha · 2016
Earlier work this paper cites.
An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
W.-L. Chao, S. Changpinyo, B. Gong, and F. Sha · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Earlier work this paper cites.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Earlier work this paper cites.
Predicting visual exemplars of unseen classes for zero-shot learning
S. Changpinyo, W.-L. Chao, and F. Sha · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Low-shot visual recognition by shrinking and hallucinating features
B. Hariharan and R. B. Girshick · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Z. Lin, M. Feng, C. N. dos Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
Cited alongside, same era.
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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Few-shot learning with graph neural networks
V. G. Satorras and J. B. Estrach · 2018
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Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
T. R. Scott, K. Ridgeway, and M. C. Mozer · 2018
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales · 2018
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Low-shot learning from imaginary data
Y.-X. Wang, R. B. Girshick, M. Hebert, and B. Hariharan · 2018
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Few-shot learning through an information retrieval lens
E. Triantafillou, R. S. Zemel, and R. Urtasun · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
Cited alongside, same era.
Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Póczos, R. R. Salakhutdinov, and A. J. Smola · 2017
Cited alongside, same era.
Domain adaption in one-shot learning
N. Dong and E. P. Xing · 2018
Cited alongside, same era.
Dropblock: A regularization method for convolutional networks
G. Ghiasi, T.-Y. Lin, and Q. V. Le · 2018
Cited alongside, same era.
Learning embedding adaptation for few-shot learning
H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha · 2018
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How to train your MAML
A. Antoniou, H. Edwards, and A. J. Storkey · 2019
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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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Unsupervised learning via meta-learning
K. Hsu, S. Levine, and C. Finn · 2019
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Meta-learning with differentiable convex optimization
K. Lee, S. Maji, A. Ravichandran, and S. Soatto · 2019
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Finding task-relevant features for few-shot learning by category traversal
H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang · 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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Transductive episodic-wise adaptive metric for few-shot learning
L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian · 2019
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Incremental few-shot learning with attention attractor networks
M. Ren, R. Liao, E. Fetaya, and R. S. Zemel · 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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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Y. Wang, W.-L. Chao, K. Q. Weinberger, and L. van der Maaten · 2019
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Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
X.-S. Wei, P. Wang, L. Liu, C. Shen, and J. Wu · 2019
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