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We present a domain adaptation based generative framework for zero-shot learning.
Caltech-ucsd birds 200
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E. Kodirov, T. Xiang, Z. Fu, and S. Gong · 2015
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B. Romera, Paredes and P. Torr · 2015
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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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Z. Zhang and V. Saligrama · 2016
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Generating visual representations for zero-shot classification
M. Bucher, S. Herbin, and F. Jurie · 2017
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Synthesizing samples for zero-shot learning
Y. Guo, G. Ding, J. Han, and Y. Gao · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell · 2017
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Zero-shot classification with discriminative semantic representation learning
M. Ye and Y. Guo · 2017
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Learning a deep embedding model for zero-shot learning
L. Zhang, T. Xiang, S. Gong, et al · 2017
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Recgan: recurrent generative adversarial networks for recommendation systems
H. Bharadhwaj, H. Park, and B. Y. Lim · 2018
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A data-efficient framework for training and sim-to-real transfer of navigation policies
H. Bharadhwaj, Z. Wang, Y. Bengio, and L. Paull · 2018
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L. Chen, H. Zhang, J. Xiao, W. Liu, and S.-F. Chang · 2018
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J. Song, C. Shen, Y. Yang, Y. Liu, and M. Song · 2018
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V. K. Verma, G. Arora, A. Mishra, and P. Rai · 2018
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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 · 2018
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