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Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel classes without any training instances.
Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Describing objects by their attributes
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
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Learning to detect unseen object classes by between-class attribute transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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Zero-shot learning with semantic output codes
M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell · 2009
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Attribute-based transfer learning for object categorization with zero/one training example
X. Yu and Y. Aloimonos · 2010
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A joint learning framework for attribute models and object descriptions
D. Mahajan, S. Sellamanickam, and V. Nair · 2011
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Evaluating knowledge transfer and zero-shot learning in a large-scale setting
M. Rohrbach, M. Stark, and B. Schiele · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Efficient max-margin multi-label classification with applications to zero-shot learning
B. Hariharan, S. Vishwanathan, and M. Varma · 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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Sun attribute database: Discovering, annotating, and recognizing scene attributes
G. Patterson and J. Hays · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Label-embedding for attribute-based classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2013
Cited alongside, same era.
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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Transfer learning in a transductive setting
M. Rohrbach, S. Ebert, and B. Schiele · 2013
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A unified probabilistic approach modeling relationships between attributes and objects
X. Wang and Q. Ji · 2013
Cited alongside, same era.
Designing category-level attributes for discriminative visual recognition
F. X. Yu, L. Cao, R. S. Feris, J. R. Smith, and S.-F. Chang · 2013
Cited alongside, same era.
Transductive multi-view embedding for zero-shot recognition and annotation
Y. Fu, T. M. Hospedales, T. Xiang, Z. Fu, and S. Gong · 2014
Unsupervised domain adaptation for zero-shot learning
E. Kodirov, T. Xiang, Z. Fu, and S. Gong · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
J. Lei Ba, K. Swersky, S. Fidler, et al · 2015
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An embarrassingly simple approach to zero-shot learning
B. Romera-Paredes and P. H. Torr · 2015
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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. Bernstein, et al · 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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Cited alongside, same era.
Zero-shot recognition with unreliable attributes
D. Jayaraman and K. Grauman · 2014
Cited alongside, same era.
Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2014
Cited alongside, same era.
Costa: Co-occurrence statistics for zero-shot classification
T. Mensink, E. Gavves, and C. G. Snoek · 2014
Cited alongside, same era.
Transfer learning based on the observation probability of each attribute
M. Suzuki, H. Sato, S. Oyama, and M. Kurihara · 2014
Cited alongside, same era.
Evaluation of output embeddings for fine-grained image classification
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele · 2015
Cited alongside, same era.
Zero-shot object recognition by semantic manifold distance
Z. Fu, T. Xiang, E. Kodirov, and S. Gong · 2015
Cited alongside, same era.
Later among the works it cites.
Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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
Later among the works it cites.