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Inductive Generalized Zero-Shot Learning with Adversarial Relation Network

Publication ,  Conference
Yang, G; Huang, K; Zhang, R; Goulermas, JY; Hussain, A
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2021

We consider the inductive Generalized Zero Shot Learning (GZSL) problem where test information is assumed unavailable during training. In lack of training samples and attributes for unseen classes, most existing GZSL methods tend to classify target samples as seen classes. To alleviate such problem, we design an adversarial Relation Network that favors target samples towards unseen classes while enjoying robust recognition for seen classes. Specifically, through the adversarial framework, we can attain a robust recognizer where a small gradient adjustment to the instance will not affect too much the classification of seen classes but substantially increase the classification accuracy on unseen classes. We conduct a series of experiments extensively on four benchmarks i.e., AwA1, AwA2, aPY, and CUB. Experimental results show that our proposed method can attain encouraging performance, which is higher than the best of state-of-the-art models by 10.8%, 14.0%, 6.9%, and 1.9% on the four benchmark datasets, respectively in the inductive GZSL scenario. (The code is available on https://github.com/ygyvsys/AdvRN-with-SR )

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030676605

Publication Date

January 1, 2021

Volume

12458 LNAI

Start / End Page

724 / 739

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Yang, G., Huang, K., Zhang, R., Goulermas, J. Y., & Hussain, A. (2021). Inductive Generalized Zero-Shot Learning with Adversarial Relation Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12458 LNAI, pp. 724–739). https://doi.org/10.1007/978-3-030-67661-2_43
Yang, G., K. Huang, R. Zhang, J. Y. Goulermas, and A. Hussain. “Inductive Generalized Zero-Shot Learning with Adversarial Relation Network.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12458 LNAI:724–39, 2021. https://doi.org/10.1007/978-3-030-67661-2_43.
Yang G, Huang K, Zhang R, Goulermas JY, Hussain A. Inductive Generalized Zero-Shot Learning with Adversarial Relation Network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 724–39.
Yang, G., et al. “Inductive Generalized Zero-Shot Learning with Adversarial Relation Network.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12458 LNAI, 2021, pp. 724–39. Scopus, doi:10.1007/978-3-030-67661-2_43.
Yang G, Huang K, Zhang R, Goulermas JY, Hussain A. Inductive Generalized Zero-Shot Learning with Adversarial Relation Network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 724–739.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783030676605

Publication Date

January 1, 2021

Volume

12458 LNAI

Start / End Page

724 / 739

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences