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We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each.
One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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One shot learning of simple visual concepts
B. Lake, R. Salakhutdinov, J. Gross, and J. Tenenbaum · 2011
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Multiclass recognition and part localization with humans in the loop
C. Wah, S. Branson, P. Perona, and S. Belongie · 2011
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Bayesian face revisited: A joint formulation
D. Chen, X. Cao, L. Wang, F. Wen, and J. Sun · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2012
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Devise: A deep visual-semantic embedding model
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, T. Mikolov, et al · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 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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Transductive multi-view embedding for zero-shot recognition and annotation
Y. Fu, T. M. Hospedales, T. Xiang, Z. Fu, and S. Gong · 2014
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Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2014
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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
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Evaluation of output embeddings for fine-grained image classification
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 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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Predicting deep zero-shot convolutional neural networks using textual descriptions
J. Lei Ba, K. Swersky, S. Fidler, and R. Salakhutdinov · 2015
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An embarrassingly simple approach to zero-shot learning
B. Romera-Paredes and P. Torr · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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A unified perspective on multi-domain and multi-task learning
Y. Yang and T. M. Hospedales · 2015
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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. Lillicrap, D. Wierstra, et al · 2016
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Latent embeddings for zero-shot classification
Y. Xian, Z. Akata, G. Sharma, Q. Nguyen, M. Hein, and B. Schiele · 2016
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Zero-shot learning via joint latent similarity embedding
Z. Zhang and V. Saligrama · 2016
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Zero-shot recognition via structured prediction
Z. Zhang and V. Saligrama · 2016
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Towards a neural statistician
H. Edwards and A. Storkey · 2017
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 2015
Cited alongside, same era.
Label-embedding for image classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2016
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Learning feed-forward one-shot learners
L. Bertinetto, J. F. Henriques, J. Valmadre, P. H. S. Torr, and A. Vedaldi · 2016
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Improving semantic embedding consistency by metric learning for zero-shot classiffication
M. Bucher, S. Herbin, and F. Jurie · 2016
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Synthesized classifiers for zero-shot learning
S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha · 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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Learning to remember rare events
Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio · 2017
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Semantic autoencoder for zero-shot learning
E. Kodirov, T. Xiang, and S. Gong · 2017
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Meta networks
T. Munkhdalai and H. Yu · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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A simple neural network module for relational reasoning
A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
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Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata · 2017
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Learning a deep embedding model for zero-shot learning
L. Zhang, T. Xiang, and S. Gong · 2017
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