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Bidirectional mapping coupled GAN for generalized Zero-Shot learning
Journal article   Peer reviewed

Bidirectional mapping coupled GAN for generalized Zero-Shot learning

T. Shermin, S.W. Teng, F. Sohel, M. Murshed and G. Lu
IEEE Transactions on Image Processing, Vol.31, pp.721-733
2022
DOI: https://doi.org/10.1109/TIP.2021.3135480
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Abstract

Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data. Therefore, learning a joint distribution of seen-unseen classes and preserving the distinction between seen-unseen classes is crucial for GZSL methods. However, existing methods only learn the underlying distribution of seen data, although unseen class semantics are available in the GZSL problem setting. Most methods neglect retaining seen-unseen classes distinction and use the learned distribution to recognize seen and unseen data. Consequently, they do not perform well. In this work, we utilize the available unseen class semantics alongside seen class semantics and learn joint distribution through a strong visual-semantic coupling. We propose a bidirectional mapping coupled generative adversarial network (BMCoGAN) by extending the concept of the coupled generative adversarial network into a bidirectional mapping model. We further integrate a Wasserstein generative adversarial optimization to supervise the joint distribution learning. We design a loss optimization for retaining distinctive information of seen-unseen classes in the synthesized features and reducing bias towards seen classes, which pushes synthesized seen features towards real seen features and pulls synthesized unseen features away from real seen features. We evaluate BMCoGAN on benchmark datasets and demonstrate its superior performance against contemporary methods.

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101 Record Views
9 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.17 Computer Vision & Graphics
4.17.2789 Generative Image Synthesis
Web Of Science research areas
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
ESI research areas
Engineering
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