1. Distance Guided Generative Adversarial Network for Explainable Binary Classifications
- Author
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Xiong, Xiangyu, Sun, Yue, Liu, Xiaohong, Ke, Wei, Lam, Chan-Tong, Chen, Jiangang, Jiang, Mingfeng, Wang, Mingwei, Xie, Hui, Tong, Tong, Gao, Qinquan, Chen, Hao, and Tan, Tao
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On the other hand, advanced generative adversarial networks (GANs) generate inter-domain samples with limited variety. These previous methods make limited contributions to describing the decision boundaries for binary classification. In this paper, we propose a distance guided GAN (DisGAN) which controls the variation degrees of generated samples in the hyperplane space. Specifically, we instantiate the idea of DisGAN by combining two ways. The first way is vertical distance GAN (VerDisGAN) where the inter-domain generation is conditioned on the vertical distances. The second way is horizontal distance GAN (HorDisGAN) where the intra-domain generation is conditioned on the horizontal distances. Furthermore, VerDisGAN can produce the class-specific regions by mapping the source images to the hyperplane. Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification. The proposed method can apply to different classification architectures and has potential to extend to multi-class classification., Comment: 12 pages, 8 figures. This work has been submitted to the IEEE TNNLS for possible publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media
- Published
- 2023