Browse > Article
http://dx.doi.org/10.9708/jksci.2020.25.05.037

MSaGAN: Improved SaGAN using Guide Mask and Multitask Learning Approach for Facial Attribute Editing  

Yang, Hyeon Seok (Dept. of Computer Science and Engineering, Hanyang University)
Han, Jeong Hoon (Dept. of Computer Science and Engineering, Hanyang University)
Moon, Young Shik (Dept. of Computer Science and Engineering, Hanyang University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.5, 2020 , pp. 37-46 More about this Journal
Abstract
Recently, studies of facial attribute editing have obtained realistic results using generative adversarial net (GAN) and encoder-decoder structure. Spatial attention GAN (SaGAN), one of the latest researches, is the method that can change only desired attribute in a face image by spatial attention mechanism. However, sometimes unnatural results are obtained due to insufficient information on face areas. In this paper, we propose an improved SaGAN (MSaGAN) using a guide mask for learning and applying multitask learning approach to improve the limitations of the existing methods. Through extensive experiments, we evaluated the results of the facial attribute editing in therms of the mask loss function and the neural network structure. It has been shown that the proposed method can efficiently produce more natural results compared to the previous methods.
Keywords
Facial attribute editing; Generative adversarial networks(GAN); SaGAN; Deep learning; Spatial attention mechanism;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 H. S. Yang and Y. S. Moon, "Face Attribute Editing using AttGAN and Guide Mask," 2019 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1-3, Jan. 2019. DOI: 10.23919/ELINFOCOM.2019. 8706471
2 S. K. Woo, "Generation of Contrast Enhanced Computed Tomography Image using Deep Learning Network," Journal of The Korea Society of Computer and Information, Vol. 24, No. 3, pp. 41-47, Mar. 2019. DOI: 10.9708/jksci.2019.24.03.041   DOI
3 A. Radford, L. Metz, and S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," arXiv preprint arXiv:1511.06434v2, pp. 1-16, Jan. 2016.
4 M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," arXiv preprint arXiv:1411.1784, pp. 1-7, Nov. 2014.
5 G. Perarnau, J. V. D. Weijer, B. Raduanu, and J. M. Alvarez, "Invertible Conditional GANs for Image Editing," NIPS 2016 Workshop on Adversarial Training, pp. 1-9, Dec. 2016.
6 P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efos, "Image-to-Image Translation with Conditional Adversarial Networks," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125-1134, Jul. 2017. DOI: 10.1109/CVPR.2017.632
7 J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks," Proceedings of the IEEE international conference on computer vision, pp. 2223-2232, Oct. 2017. DOI: 10.1109/ICCV.2017.244
8 Y. Choi, M. Choi, M. Kim, J. W. Ha, S. Kim, and J. Choo, "StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8789-8797, Jun. 2018. DOI: 10.1109/CVPR.2018.00916
9 Z. Liu, P. Luo, X. Wang, and X. Tang, "Deep Learning Face Attributes in the Wild," Proceedings of the IEEE International Conference on Computer Vision, pp. 3730-3738, Dec. 2015. DOI: 10.1109/ICCV.2015.425
10 C. H. Lee, Z. Liu, L. Wu, and P. Luo, "MaskGAN: Towards Diverse and Interactive Facial Image Manipulation," arXiv preprint arXiv:1907.11922v2, pp. 1-20, Apr. 2020.
11 I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative Adversarial Nets," Advances in neural information processing systems, pp. 2672-2680, Dec. 2014.
12 X. Zheng, Y. Guo, H. Huang, Y. Li, and R. He, "A Survey to Deep Facial Attribute Analysis," International Journal of Computer Vision, pp. 1-33, Mar. 2020. DOI: 10.1007/s11263-020-01308-z
13 T. J. Choi and H. M. Lee "An Algorithm for Converting 2D Face Image into 3D Model," Journal of The Korea Society of Computer and Information, Vol. 20, No. 4, pp. 41-48, Apr. 2015. DOI: 10.9708/jksci.2015.20.4.041   DOI
14 S. C. Bae, Y. S. Lee, and S. W. Choi "Vision-based Authentication and Registration of Facial Identity in Hospital Information System," Journal of The Korea Society of Computer and Information, Vol. 24, No. 12, pp. 59-65, Dec. 2019. DOI: 10.9708/jksci.2019.24.12.059   DOI
15 W. Shen and R. Liu, "Learning Residual Images for Face Attribute Manipulation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4030-4038, Jul. 2017. DOI: 10.1109/CVPR.2017.135
16 H. S. Yang, J. H. Han, Y. C. Cho, H. G. Lee, Y. Park, and Y. S. Moon, "Study on Performance Improvement of SAGAN using Mask," Proceeding of 2019 Korea Signal Processing Conference, pp. 2557-2560, Sep. 2019.
17 G. Zhang, M. Kan, S. Shan, and X. Chen, "Generative Adversarial Network with Spatial Attention for Face Attribute Editing," Proceedings of the European Conference on Computer Vision (ECCV), pp. 417-432, Sep. 2018. DOI: 10.1007/978-3-030-01231-1_26
18 Z. He, W. Zuo, and S. Shan, "AttGAN: Facial Attribute Editing by Only Changing What You Want." IEEE Transactions on Image Processing, Vol. 28, No. 11, pp. 5464-5478, May. 2019. DOI: 10.1109/TIP.2019.2916751   DOI