Conditional Deep Convolutional Generative Adversarial Networks for Isolated Handwritten Arabic Character Generation

2021 ◽  
Author(s):  
Ismail B. Mustapha ◽  
Shafaatunnur Hasan ◽  
Hatem Nabus ◽  
Siti Mariyam Shamsuddin
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Handwritten Arabic

scholarly journals GEAC: Generating and Evaluating Handwritten Arabic Characters Using Generative Adversarial Networks

2020 ◽  
Author(s):  
Tarik Alafif
Keyword(s):  
Research Work ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Generative Adversarial Network ◽  
Research Areas ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
A Value ◽  
Arabic Characters ◽  
Handwritten Arabic

Generative Adversarial Network (GAN) has made a breakthrough and great success in many research areas in computer vision. Different GANs generate different outputs. In this research work, we apply different GANs to generate handwritten Arabic characters. A basic GAN, Vanilla GAN, Deep Convolutional GAN (DCGAN), Bidirectional GAN (BiGAN), and Wasserstein GAN (WGAN) are used. Then, the results of the generated images are evaluated using native-Arabic human and Fréchet Inception Distance (FID). The qualitative and quantitative results are provided for the images generation and evaluation. In experimental evaluation, WGAN achieves better results in FID with a value of 96.007. On the other hand, DCGAN achieves better results in native-Arabic human evaluation with a value of 35%.

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.

A Primer on Generative Adversarial Networks

2020 ◽  
Author(s):  
Dr. Vikas Thada ◽  
Mr. Utpal Shrivastava ◽  
Jyotsna Sharma ◽  
Kuwar Prateek Singh ◽  
Manda Ranadeep
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks
Sign in / Sign up

Export Citation Format

Share Document