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Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.
Is learning the n-th thing any easier than learning the first?
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Cross-generalization: Learning novel classes from a single example by feature replacement
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Object classification from a single example utilizing class relevance metrics
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One-shot learning of object categories
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Generative adversarial nets
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Auto-encoding variational Bayes
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D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhudtinov · 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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A simple way to initialize recurrent networks of rectified linear units
Q. V. Le, N. Jaitly, and G. E. Hinton · 2015
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FaceNet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. P. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
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Learning from small sample sets by combining unsupervised meta-training with CNNs
Y.-X. Wang and M. Hebert · 2016
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Learning to learn: Model regression networks for easy small sample learning
Y.-X. Wang and M. Hebert · 2016
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Good semi-supervised learning that requires a bad GAN
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. Salakhutdinov · 2017
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AGA: Attribute-Guided Augmentation
M. Dixit, R. Kwitt, M. Niethammer, and N. Vasconcelos · 2017
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Web-scale training for face identification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2015
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One shot learning via compositions of meaningful patches
A. Wong and A. L. Yuille · 2015
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Learning feed-forward one-shot learners
L. Bertinetto, J. Henriques, J. Valmadre, P. Torr, and A. Vedaldi · 2016
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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
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Towards a neural statistician
H. Edwards and A. Storkey · 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 generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs
D. George, W. Lehrach, K. Kansky, M. Lázaro-Gredilla, C. Laan, B. Marthi, X. Lou, Z. Meng, Y. Liu, H. Wang, A. Lavin, and D. S. Phoenix · 2017
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Low-shot visual recognition by shrinking and hallucinating features
B. Hariharan and R. Girshick · 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. S. Zemel · 2017
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Learning to model the tail
Y.-X. Wang, D. Ramanan, and M. Hebert · 2017
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