NDLI: Regional attention generative adversarial network

Content Provider	IET Digital Library
Author	Wang, Wei Hu, Haifeng Huang, Yi Ruan, Chongchong Chen, Dihu
Abstract	In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN. The proposed mechanism can build dependencies between the high-level representations extracted from attention regions of real images and corresponding feature maps of the generative network. By modelling these dependencies, the generative network can be facilitated to learn feature mapping and fit the distribution of real data. They conduct extensive experiments on widely used datasets CIFAR-10, STL-10, and CelebA. The quantitative and qualitative performance improvement over state-of-the-art methods demonstrates the validity of the proposed attention mechanism in improving the quality of generated images.
Starting Page	459
Ending Page	461
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 8, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2018.8251
Journal	Electronics Letters
Publisher Date	2019-03-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Attention Mechanism Attention Model Regional Attention GANs Attention Regions Classical Generative Adversarial Network Model Computer Vision And Image Processing Technique Corresponding Feature Maps Dependencies Feature Extraction Feature Mapping GAN Generated Image Generated Sample Generative Network High-level Representations Image And Video Coding Image Classification Image Coding Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Object Detection Optical, Image And Video Signal Processing Qualitative Performance Improvement Quantitative Performance Improvement Regional Attention Generative Adversarial Network Unsupervised Learning Visual Quality
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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