TY - GEN
T1 - On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning
AU - Shim, Joo Yong
AU - Kim, Joongheon
AU - Kim, Jong Kook
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by National Research Foundation of Korea (2019R1A2C4070663, 2019M3E4A1080391). J. Kim and J.-K. Kim are the corresponding authors of this paper.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/13
Y1 - 2021/1/13
N2 - The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.
AB - The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.
UR - http://www.scopus.com/inward/record.url?scp=85100795277&partnerID=8YFLogxK
U2 - 10.1109/ICOIN50884.2021.9333991
DO - 10.1109/ICOIN50884.2021.9333991
M3 - Conference contribution
AN - SCOPUS:85100795277
T3 - International Conference on Information Networking
SP - 422
EP - 424
BT - 35th International Conference on Information Networking, ICOIN 2021
PB - IEEE Computer Society
T2 - 35th International Conference on Information Networking, ICOIN 2021
Y2 - 13 January 2021 through 16 January 2021
ER -