scholarly journals End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks

2021 ◽  
Author(s):  
Alice Xue
Keyword(s):  
Generative Adversarial Networks ◽  
Landscape Painting ◽  
Adversarial Networks ◽  
End To End ◽  
Chinese Landscape Painting

scholarly journals FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing

2020 ◽  
Vol 34 (07) ◽  
pp. 10729-10736 ◽  
Author(s):  
Yu Dong ◽  
Yihao Liu ◽  
He Zhang ◽  
Shifeng Chen ◽  
Yu Qiao
Keyword(s):  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Single Image ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Color Distortion ◽  
Adversarial Networks ◽  
End To End ◽  
Single Image Dehazing

Recently, convolutional neural networks (CNNs) have achieved great improvements in single image dehazing and attained much attention in research. Most existing learning-based dehazing methods are not fully end-to-end, which still follow the traditional dehazing procedure: first estimate the medium transmission and the atmospheric light, then recover the haze-free image based on the atmospheric scattering model. However, in practice, due to lack of priors and constraints, it is hard to precisely estimate these intermediate parameters. Inaccurate estimation further degrades the performance of dehazing, resulting in artifacts, color distortion and insufficient haze removal. To address this, we propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Moreover, we synthesize a large-scale training dataset including various indoor and outdoor hazy images to boost the performance and we reveal that for learning-based dehazing methods, the performance is strictly influenced by the training data. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.

Missing value imputation in multivariate time series with end-to-end generative adversarial networks

2021 ◽  
Vol 551 ◽  
pp. 67-82
Author(s):  
Ying Zhang ◽  
Baohang Zhou ◽  
Xiangrui Cai ◽  
Wenya Guo ◽  
Xiaoke Ding ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Generative Adversarial Networks ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Adversarial Networks ◽  
End To End

scholarly journals License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images

2020 ◽  
Vol 10 (8) ◽  
pp. 2780
Author(s):  
Byung-Gil Han ◽  
Jong Taek Lee ◽  
Kil-Taek Lim ◽  
Doo-Hyun Choi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Generative Adversarial Networks ◽  
License Plate ◽  
Image Generation ◽  
Adversarial Networks ◽  
Vehicle Registration ◽  
End To End ◽  
Vehicle Information ◽  
Public Datasets

License Plate Character Recognition (LPCR) is a technology for reading vehicle registration plates using optical character recognition from images and videos, and it has a long history due to its usefulness. While LPCR has been significantly improved with the advance of deep learning, training deep networks for LPCR module requires a large number of license plate (LP) images and their annotations. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. Therefore, collecting a sufficient number of LP images is extremely difficult for normal research. In this paper, we propose LP-GAN, an LP image generation method, by applying an ensemble of generative adversarial networks (GAN), and we also propose a modified lightweight YOLOv2 model for an efficient end-to-end LPCR module. With only 159 real LP images available online, thousands of synthetic LP images were generated by using LP-GAN. The generated images not only looked similar to real ones, but they were also shown to be effective for training the LPCR module. As a result of performance tests with 22,117 real LP images, the LPCR module trained with only the generated synthetic dataset achieved 98.72% overall accuracy, which is comparable to that of training with a real LP image dataset. In addition, we improved the processing speed of LPCR about 1.7 times faster than that of the original YOLOv2 model by using the proposed lightweight model.

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