TY - GEN
T1 - Inductive Generalized Zero-Shot Learning with Adversarial Relation Network
AU - Yang, Guanyu
AU - Huang, Kaizhu
AU - Zhang, Rui
AU - Goulermas, John Y.
AU - Hussain, Amir
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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
AB - 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
KW - Adversarial examples
KW - Gradient penalty
KW - Zero-shot learning
UR - http://www.scopus.com/inward/record.url?scp=85103243254&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-67661-2_43
DO - 10.1007/978-3-030-67661-2_43
M3 - Conference Proceeding
AN - SCOPUS:85103243254
SN - 9783030676605
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 724
EP - 739
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
A2 - Hutter, Frank
A2 - Kersting, Kristian
A2 - Lijffijt, Jefrey
A2 - Valera, Isabel
PB - Springer Science and Business Media Deutschland GmbH
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
Y2 - 14 September 2020 through 18 September 2020
ER -