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Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

by 1, 1,*, 1, 1,2, 1 and 3
1
School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing 100083, China
2
Demonstration Center of Experimental Teaching in Comprehensive Engineering, Beijing Union University, Beijing 100101, China
3
Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of our published in BICS2018.
Symmetry 2018, 10(12), 734; https://doi.org/10.3390/sym10120734
Received: 8 November 2018 / Revised: 29 November 2018 / Accepted: 6 December 2018 / Published: 8 December 2018
Augmented Reality (AR) is crucial for immersive Human–Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry. Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems. View Full-Text
Keywords: object recognition; image data synthesizing; human–computer interaction; data synthesizing for immersive HCI; generative adversarial nets; background augmentation generative adversarial networks (BAGANs) object recognition; image data synthesizing; human–computer interaction; data synthesizing for immersive HCI; generative adversarial nets; background augmentation generative adversarial networks (BAGANs)
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Ma, Y.; Liu, K.; Guan, Z.; Xu, X.; Qian, X.; Bao, H. Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing. Symmetry 2018, 10, 734.

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