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Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence.
Is learning the n-th thing any easier than learning the first?
S. Thrun · 1996
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning from one example through shared densities on transforms
E. G. Miller, N. E. Matsakis, and P. A. Viola · 2000
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Cross-generalization: Learning novel classes from a single example by feature replacement
E. Bart and S. Ullman · 2005
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Object classification from a single example utilizing class relevance metrics
M. Fink · 2005
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Incremental learning of object detectors using a visual shape alphabet
A. Opelt, A. Pinz, and A. Zisserman · 2006
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Unsupervised learning of feature hierarchies
M. Ranzato · 2009
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Meaning and compositionality as statistical induction of categories and constraints
L. A. Schmidt · 2009
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Attribute-centric recognition for cross-category generalization
A. Farhadi, I. Endres, and D. Hoiem · 2010
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Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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One-shot learning with a hierarchical nonparametric bayesian model
R. Salakhutdinov, J. Tenenbaum, and A. Torralba · 2012
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Write a classifier: Zero-shot learning using purely textual descriptions
M. Elhoseiny, B. Saleh, and A. Elgammal · 2013
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DeViSE: A deep visual-semantic embedding model
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov · 2013
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Latent task adaptation with large-scale hierarchies
Y. Jia and T. Darrell · 2013
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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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Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C. D. Manning, and A. Ng · 2013
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
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DeCAF: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2014
An embarrassingly simple approach to zero-shot learning
B. Romera-Paredes and P. Torr · 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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One shot learning via compositions of meaningful patches
A. Wong and A. L. Yuille · 2015
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Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 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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Zero-shot learning by convex combination of semantic embeddings
M. Norouzi, T. Mikolov, S. Bengio, Y. Singer, J. Shlens, A. Frome, G. S. Corrado, and J. Dean · 2014
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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CNN features off-the-shelf: An astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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From generic to specific deep representations for visual recognition
H. Azizpour, A. Sharif Razavian, J. Sullivan, A. Maki, and S. Carlsson · 2015
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Discovering hidden factors of variation in deep networks
B. Cheung, J. A. Livezey, A. K. Bansal, and B. A. Olshausen · 2015
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Prototypical priors: From improving classification to zero-shot learning
S. Jetley, B. Romera-Paredes, S. Jayasumana, and P. Torr · 2015
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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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Reducing overfitting in deep networks by decorrelating representations
M. Cogswell, F. Ahmed, R. Girshick, L. Zitnick, and D. Batra · 2016
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A theoretical framework for deep transfer learning
T. Galanti, L. Wolf, and T. Hazan · 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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Metric learning with adaptive density discrimination
O. Rippel, M. Paluri, P. Dollar, and L. Bourdev · 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 to learn: Model regression networks for easy small sample learning
Y.-X. Wang and M. Hebert · 2016
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AGA: Attribute-Guided Augmentation
M. Dixit, R. Kwitt, M. Niethammer, and N. Vasconcelos · 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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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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