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Zero-shot learning, which aims to recognize new categories that are not included in the training set, has gained popularity owing to its potential ability in the real-word applications.
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2015
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2019
Closest in time.
Y. Xian, S. Sharma, B. Schiele, and Z. Akata, “f-vaegan-d2: A feature generating framework for any-shot learning,” 2019
2019
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H. Zhang, Y. Long, Y. Guan, and L. Shao, “Triple verification network for generalized zero-shot learning,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 506–517, 2019
2019
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Z. Ding and H. Liu, “Marginalized latent semantic encoder for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6191–6199
2019
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E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata, “Generalized zero-and few-shot learning via aligned variational autoencoders,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8247–8255
2019
Closest in time.
H. Huang, C. Wang, P. S. Yu, and C.-D. Wang, “Generative dual adversarial network for generalized zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 801–810
2019
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M. Bulent Sariyildiz and R. Gokberk Cinbis, “Gradient matching generative networks for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2168–2178
2019
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J. Li, M. Jin, K. Lu, Z. Ding, L. Zhu, and Z. Huang, “Leveraging the invariant side of generative zero-shot learning,” 2019
2019
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G.-S. Xie, L. Liu, X. Jin, F. Zhu, Z. Zhang, J. Qin, Y. Yao, and L. Shao, “Attentive region embedding network for zero-shot learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9384–9393
2019
Closest in time.