Improved Training of Spectral Normalization Generative Adversarial Networks

2020 ◽  
Author(s):  
Chao Xiaopeng ◽  
Cao Jiangzhong ◽  
Lu Yuqin ◽  
Dai Qingyun
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Spectral Normalization

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.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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