On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces

Wenze Shao; Jingjing Xu; Long Chen; Qi Ge; Liqian Wang; Bing‐Kun Bao; Haibo Li

doi:10.1016/j.neucom.2019.07.046

ScienceGate Book Chapters

JOURNAL ARTICLE

On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces

Wenze Shao Jingjing Xu Long Chen Qi Ge Liqian Wang Bing‐Kun Bao Haibo Li

Year: 2019 Journal: Neurocomputing Vol: 364 Pages: 1-15 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2019.07.046

Get Full-Text PDF Get Analytical Report

Keywords:

Hallucinating Computer science Artificial intelligence Face hallucination Upsampling Discriminator Generative adversarial network Subtractive color Generative grammar Face (sociological concept) Regularization (linguistics) Pattern recognition (psychology) Computer vision Machine learning Deep learning Image (mathematics) Facial recognition system Face detection Detector

Metrics

Cited By

0.75

FWCI (Field Weighted Citation Impact)

114

Refs

0.75

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces

Metrics

Citation History

Topics

Related Documents

Tiny face hallucination via boundary equilibrium generative adversarial networks

Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image Generation

Inferential Wasserstein Generative Adversarial Networks

Uncouple Generative Adversarial Networks for Transferring Stylized Portraits to Realistic Faces

Wasserstein generative adversarial networks for topology optimization