Label-Guided Generative Adversarial Network for Realistic Image Synthesis-Reference-Cited by-同舟云学术

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Label-Guided Generative Adversarial Network for Realistic Image Synthesis

Published:2022 Issue: Volume: Page:1-24
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Zhu Junchen¹^ORCID,Gao Lianli¹^ORCID,Song Jingkuan¹^ORCID,Li Yuan-Fang²^ORCID,Zheng Feng¹^ORCID,Li Xuelong³^ORCID,Shen Heng Tao¹^ORCID

Affiliation:

1. Future Media Center and School of Computer Science and Engineering, The University of Electronic Science and Technology of China, Chengdu, China

2. Faculty of Information Technology, Monash University, Australia

3. School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, P. R. China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/09810175.pdf?arnumber=9810175

Reference62 articles.

1. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

2. Unsupervised image-to-image translation networks;liu;Proc Int Conf Neural Inf Process,2017

3. Dilated Residual Networks

4. Multimodal unsupervised image-to-image translation;huang;Proc Eur Conf Comput Vis,2018

5. Gans trained by a two time-scale update rule converge to a local nash equilibrium;heusel;Proc Int Conf Neural Inf Process,2017

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