Results 321 to 330 of about 222,396 (351)

DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization

Computer Vision and Pattern Recognition, 2020
Images can be manipulated for nefarious purposes to hide content or to duplicate certain objects through copy-move operations. Discovering a well-crafted copy-move forgery in images can be very challenging for both humans and machines; for example, an ...
Ashraful Islam, Chengjiang Long, Arslan Basharat, A. Hoogs   +3 more
semanticscholar   +1 more source

Generative Adversarial and Self-Supervised Dehazing Network

IEEE Transactions on Industrial Informatics
Owing to the fast developments of economics, a lot of devices and objects have been connected and have formed the Internet of Things (IoT). Visual sensors have been applied in vehicle navigation, traffic situational awareness, and traffic safety ...
Shengdong Zhang, Xiaoqin Zhang, Shaohua Wan, Wenqi Ren, Liping Zhao, Linlin Shen   +5 more
semanticscholar   +1 more source

BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network

ACM Multimedia, 2018
Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity.
Tingting Li, Ruihe Qian, Chao Dong, Si Liu, Qiong Yan, Wenwu Zhu, Liang Lin   +6 more
semanticscholar   +1 more source

A Novel Pattern for Infrared Small Target Detection With Generative Adversarial Network

IEEE Transactions on Geoscience and Remote Sensing, 2020
Since existing detectors are often sensitive to the complex background, a novel detection pattern based on generative adversarial network (GAN) is proposed to focus on the essential features of infrared small target in this article. Motivated by the fact
Bin Zhao, Chunping Wang, Qiang Fu, Zishuo Han   +3 more
semanticscholar   +1 more source

Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

IEEE Transactions on Geoscience and Remote Sensing, 2020
The rich and distinguishable spectral information in hyperspectral images (HSIs) makes it possible to capture anomalous samples [i.e., anomaly detection (AD)] that deviate from background samples.
T. Jiang, Yunsong Li, Weiying Xie, Q. Du
semanticscholar   +1 more source

ARShadowGAN: Shadow Generative Adversarial Network for Augmented Reality in Single Light Scenes

Computer Vision and Pattern Recognition, 2020
Generating virtual object shadows consistent with the real-world environment shading effects is important but challenging in computer vision and augmented reality applications.
Daquan Liu, Chengjiang Long, Hongpan Zhang, Hanning Yu, Xinzhi Dong, Chunxia Xiao   +5 more
semanticscholar   +1 more source

Image Blind Denoising with Generative Adversarial Network Based Noise Modeling

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not
Jingwen Chen, Jiawei Chen, Hongyang Chao, Ming Yang   +3 more
semanticscholar   +1 more source

DeepPrivacy: A Generative Adversarial Network for Face Anonymization

International Symposium on Visual Computing, 2019
We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information ...
Håkon Hukkelås, R. Mester, F. Lindseth
semanticscholar   +1 more source

Generative Adversarial Networks

2018
For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al.
Mathew Salvaris, Danielle Dean, Wee Hyong Tok   +2 more
openaire   +1 more source

Generative Adversarial Network

2020
Generative adversarial networks (GANs) are a type of deep learning model designed by Ian Goodfellow and his colleagues in 2014.
openaire   +1 more source