Structure Improved Conditional Spectral Normalization Generative Adversarial Networks for Image Recognition

2020 ◽  
Author(s):  
Tao Xie ◽  
Yiming DU ◽  
Ting Wang ◽  
Bohui Hao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Image Recognition ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Spectral Normalization

scholarly journals Enhanced network optimized generative adversarial network for image enhancement

2021 ◽  
Author(s):  
Lingyu Yan ◽  
Jiarun Fu ◽  
Chunzhi Wang ◽  
Zhiwei Ye ◽  
Hongwei Chen ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Image Recognition ◽  
Generative Adversarial Networks ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Enhancement Method ◽  
New Space ◽  
Traditional Image

AbstractWith the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

scholarly journals Lipschitz constrained GANs via boundedness and continuity

2020 ◽  
Vol 32 (24) ◽  
pp. 18271-18283
Author(s):  
Kanglin Liu ◽  
Guoping Qiu
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Convolutional Neural Network ◽  
Generative Adversarial Networks ◽  
Heuristic Methods ◽  
Performance Control ◽  
Effective Implementation ◽  
Adversarial Networks ◽  
Spectral Normalization

AbstractOne of the challenges in the study of generative adversarial networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity (BC) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the BC conditions (BC–GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC–GANs have not only better performances but also lower computational complexity.

scholarly journals Icon Generation Based on Generative Adversarial Networks

2021 ◽  
Vol 11 (17) ◽  
pp. 7890
Author(s):  
Hongyi Yang ◽  
Chengqi Xue ◽  
Xiaoying Yang ◽  
Han Yang
Keyword(s):  
User Study ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Learning Technology ◽  
Ui Design ◽  
Training Techniques ◽  
Adversarial Networks ◽  
Art Research ◽  
Spectral Normalization ◽  
Degree Of Innovation

Icon design is an important part of UI design, and a design task that designers often encounter. During the design process, it is important to highlight the function of icons themselves and avoid excessive similarity with similar icons, i.e., to have a certain degree of innovation and uniqueness. With the rapid development of deep learning technology, generative adversarial networks (GANs) can be used to assist designers in designing and updating icons. In this paper, we construct an icon dataset consisting of 8 icon categories, and introduce state-of-the-art research and training techniques including attention mechanism and spectral normalization based on the original StyleGAN. The results show that our model can effectively generate high-quality icons. In addition, based on the user study, we demonstrate that our generated icons can be useful to designers as design aids. Finally, we discuss the potential impacts and consider the prospects for future related research.

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