A Heavy-tailed Distribution Data Generation Method Based On Generative Adversarial Network

2021 ◽  
Author(s):  
Xiaoyu Zhang ◽  
Jinglin Zhou
Keyword(s):  
Distribution Data ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Heavy Tailed Distribution ◽  
Heavy Tailed

scholarly journals OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

2021 ◽  
Author(s):  
Petr Marek ◽  
Vishal Ishwar Naik ◽  
Anuj Goyal ◽  
Vincent Auvray
Keyword(s):  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals A Review of Tabular Data Synthesis Using GANs on an IDS Dataset

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 375
Author(s):  
Stavroula Bourou ◽  
Andreas El Saer ◽  
Terpsichori-Helen Velivassaki ◽  
Artemis Voulkidis ◽  
Theodore Zahariadis
Keyword(s):  
Machine Learning ◽  
Defense Mechanism ◽  
Learning Tools ◽  
Data Generation ◽  
Tabular Data ◽  
Network Systems ◽  
Generative Adversarial Network ◽  
Data Synthesis ◽  
Detection Systems ◽  
Adversarial Network

Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently, which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN), and it has great potential for tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN, and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN, and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on an NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods, we argue and evaluate the most prominent GANs for tabular network data synthesis.

Actual Image Data Generation and Application via Generative Adversarial Network

2020 ◽  
Author(s):  
Ruiming Li ◽  
Jinxin Wang ◽  
Wenlve Zhou ◽  
Xi Wu ◽  
Chaojun Dong ◽  
...  
Keyword(s):  
Image Data ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Human Face Generation using Deep Convolution Generative Adversarial Network

2021 ◽  
Vol 7 (01) ◽  
pp. 114-120
Author(s):  
Chaudhary Sarimurrab, Ankita Kesari Naman and Sudha Narang
Keyword(s):  
Random Noise ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Practical Applications ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Face Generation ◽  
Human Faces

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. In this model, we aim to generate human faces through un-labelled data via the help of Deep Convolutional Generative Adversarial Networks. The applications for generating faces are vast in the field of image processing, entertainment, and other such industries. Our resulting model is successfully able to generate human faces from the given un-labelled data and random noise.

Generation of Synthetic Data with Conditional Generative Adversarial Networks

2020 ◽  
Author(s):  
Belén Vega-Márquez ◽  
Cristina Rubio-Escudero ◽  
Isabel Nepomuceno-Chamorro
Keyword(s):  
Research Work ◽  
Synthetic Data ◽  
Original Data ◽  
Classification Problem ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset

Abstract The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.

scholarly journals Synthesizing electronic health records using improved generative adversarial networks

2018 ◽  
Vol 26 (3) ◽  
pp. 228-241 ◽  
Author(s):  
Mrinal Kanti Baowaly ◽  
Chia-Ching Lin ◽  
Chao-Lin Liu ◽  
Kuan-Ta Chen
Keyword(s):  
Electronic Health Records ◽  
Binary Data ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Research Database ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Health Records ◽  
Adversarial Network ◽  
Electronic Health

AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models’ performance by using a few statistical methods (Kolmogorov–Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction).ResultsWe conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best.DiscussionTo generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics.ConclusionThe proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.

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