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Generalized zero shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen classes, while the testing process consists of the classification of visual samples from seen and unseen classes.
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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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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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M. Bucher, S. Herbin, and F. Jurie · 2017
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Y. Xian, B. Schiele, and Z. Akata · 2017
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Multi-modal cycle-consistent generalized zero-shot learning
R. Felix, B. V. Kumar, I. Reid, and G. Carneiro · 2018
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Pseudo transfer with marginalized corrupted attribute for zero-shot learning
T. Long, X. Xu, Y. Li, F. Shen, J. Song, and H. T. Shen · 2018
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Generalized zero-shot learning via synthesized examples
V. K. Verma, G. Arora, A. Mishra, and P. Rai · 2018
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Feature generating networks for zero-shot learning
Y. Xian, T. Lorenz, B. Schiele, and Z. Akata · 2018
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