Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation
نویسندگان
چکیده
منابع مشابه
Attention-Aware Generative Adversarial Networks (ATA-GANs)
In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a TeacherNetwork we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images. To this end, we generate images of HEp-2 cells captured with Indirect Imunofluoresence (IIF) and study the ability of ...
متن کاملSurvey on Generative Adversarial Networks
Generative Adversarial Networks or GANs were introduced by Ian Goodfellow and his colleagues at the university of Montreal. The concept behind these networks is that, two models fighting against each other would be able to co-train and eventually create a system that could learn more, with less help from humans, effectively reducing the huge amount of human effort required in training and creat...
متن کاملGANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference
We introduce a framework using Generative Adversarial Networks (GANs) for likelihood–free inference (LFI) and Approximate Bayesian Computation (ABC). Our approach addresses both the key problems in likelihood–free inference, namely how to compare distributions and how to efficiently explore the parameter space. Our framework allows one to use the simulator model as a black box and leverage the ...
متن کاملAdversarial Examples Generation and Defense Based on Generative Adversarial Network
We propose a novel generative adversarial network to generate and defend adversarial examples for deep neural networks (DNN). The adversarial stability of a network D is improved by training alternatively with an additional network G. Our experiment is carried out on MNIST, and the adversarial examples are generated in an efficient way compared with wildly-used gradient based methods. After tra...
متن کاملGang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
Traditional generative adversarial networks (GAN) and many of its variants are trained by minimizing the KL or JS-divergence loss that measures how close the generated data distribution is from the true data distribution. A recent advance called the WGAN based on Wasserstein distance can improve on the KL and JS-divergence based GANs, and alleviate the gradient vanishing, instability, and mode ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Machine Learning and Computing
سال: 2022
ISSN: ['2010-3700']
DOI: https://doi.org/10.18178/ijmlc.2022.12.6.1120