TY - GEN
T1 - Face reconstruction with generative adversarial network
AU - Putra, Dino Hariatma
AU - Basaruddin, T.
N1 - Funding Information:
We would like to thank Universitas Indonesia for the support in this research through PITTA (Publikasi Terindeks International untuk Tugas Akhir Mahasiswa UI/ International Indexed Publication for UI Student’s Final Assignments) grant no. 1893/UN2.R3.1/HKP.05.00/2018.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/1/25
Y1 - 2019/1/25
N2 - Generative Adversarial Network (GAN) is a framework of deep learning in generative models. The generative model aims to synthesize a new data so that it has a distribution of distribution according to the original data distribution. In the current development, GAN is not only used to synthesize data from noise alone, but in the current development it has begun to be used to translate data from a domain to data with a different domain. Several studies have been developed, such as CycleGAN, and Pix2pix. In this study, the face has not been used as an object of translation. In this study a model for translating images of face sketches into face images will be made.
AB - Generative Adversarial Network (GAN) is a framework of deep learning in generative models. The generative model aims to synthesize a new data so that it has a distribution of distribution according to the original data distribution. In the current development, GAN is not only used to synthesize data from noise alone, but in the current development it has begun to be used to translate data from a domain to data with a different domain. Several studies have been developed, such as CycleGAN, and Pix2pix. In this study, the face has not been used as an object of translation. In this study a model for translating images of face sketches into face images will be made.
KW - CycleGAN
KW - Deep learning
KW - Face image
KW - Face sketch
KW - Generative adversarial network
KW - Synthesize
KW - Translation
UR - http://www.scopus.com/inward/record.url?scp=85065202870&partnerID=8YFLogxK
U2 - 10.1145/3310986.3311008
DO - 10.1145/3310986.3311008
M3 - Conference contribution
AN - SCOPUS:85065202870
T3 - ACM International Conference Proceeding Series
SP - 181
EP - 185
BT - ICMLSC 2019 - Proceedings of the 3rd International Conference on Machine Learning and Soft Computing
PB - Association for Computing Machinery
T2 - 3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019
Y2 - 25 January 2019 through 28 January 2019
ER -