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Many recent methods of zero-shot learning (ZSL) attempt to utilize generative model to generate the unseen visual samples from semantic descriptions and random noise.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, , and William T Freeman · 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
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Attribute-centric recognition for cross-category generalization
Ali Farhadi, Ian Endres, and Derek Hoiem · 2010
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The caltech-ucsd birds-200-2011 dataset
C. Wah, P. Welinder S. Branson, P. Perona, and S. Belongie · 2011
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Sun attribute database: Discovering, annotating, and recognizing scene attributes
G. Patterson and J. Hays · 2012
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Transductive multi-view embedding for zero-shot recognition and annotation
Yanwei Fu, Timothy M Hospedales, Tao Xiang, Zhenyong Fu, and Shaogang Gong · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets, 2014
Mehdi Mirza and Simon Osindero · 2014
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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
Earlier work this paper cites.
Unsupervised domain adaptation for zero-shot learning
Elyor Kodirov, Tao Xiang, Zhenyong Fu, and Shaogang Gong · 2015
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Ridge regression and hubness and zero-shot learning
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto · 2015
Earlier work this paper cites.
Zero-shot learning via semantic similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2015
Earlier work this paper cites.
Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 2015
Cited alongside, same era.
Label-embedding for image classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2016
Cited alongside, same era.
Synthesized classifiers for zero-shot learning
S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
Learning a deep embedding model for zero-shot learning
Li Zhang, Tao Xiang, and Shaogang Gong · 2017
Later among the works it cites.
Preserving semantic relations for zero-shot learning
Y. Annadani and S. Biswas · 2018
Later among the works it cites.
Generalized zero-shot learning via synthesized examples
V Kumar Verma, Gundeep Arora, Ashish Mishra, and Piyush Rai · 2018
Later among the works it cites.
Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly
Xian, Yongqin, Lampert Christoph H., Schiele Bernt, and Akata Zeynep · 2018
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Feature generating networks for zero-shot learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata · 2018
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Densely connected pyramid dehazing network
H. Zhang and V. M. Patel · 2018
Later among the works it cites.
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Cited alongside, same era.
Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
Cited alongside, same era.
Zero-shot learning via joint latent similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
Cited alongside, same era.
Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Generating visual representations for zero-shot classification
Maxime Bucher, Stephane Herbin, and Frederic Jurie · 2017
Cited alongside, same era.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
Cited alongside, same era.
Photographic text-to-image synthesis with a hierarchically-nested adversarial network
Z. Zhang, Y. Xie, and L. Yang · 2018
Later among the works it cites.
Translating and segmenting multimodal medical volumes with cycle- and shapeconsistency generative adversarial network
Z. Zhang, L. Yang, and Y. Zheng · 2018
Later among the works it cites.
A generative adversarial approach for zero-shot learning from noisy texts
Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, and Ahmed Elgammal · 2018
Later among the works it cites.
Zero-shot image recognition using relational matching, adaptation and calibration
D. Das and C. S. George Lee · 2019
Closest in time.
Generative dual adversarial network for generalized zero-shot learning
He Huang, Changhu Wang, Philip S. Yu, and Chang-Dong Wang · 2019
Closest in time.
Leveraging the invariant side of generative zero-shot learning
Jingjing Li, Mengmeng Jin, Ke Lu, Zhengming Ding, Lei Zhu, and Zi Huang · 2019
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Gradient matching generative networks for zero-shot learning
Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis · 2019
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