-
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Generative adversarial networks (GANs) have demonstrated to be successfu...
read it
-
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network
Generative adversarial networks (GANs) have been recently adopted for su...
read it
-
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
In this paper we propose a data augmentation method for time series with...
read it
-
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with bio...
read it
-
FedGAN: Federated Generative Adversarial Networks for Distributed Data
We propose Federated Generative Adversarial Network (FedGAN) for trainin...
read it
-
Conditional GAN for timeseries generation
It is abundantly clear that time dependent data is a vital source of inf...
read it
-
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation
For health prognostic task, ever-increasing efforts have been focused on...
read it
Generative adversarial network based on chaotic time series
Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator and discriminator are evolving through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution, highly realistic scenes, human faces, among others have been generated. While GAN in general needs a large amount of genuine training data sets, it is noteworthy that vast amounts of pseudorandom numbers are required. Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data. We show that the similarity in proximity, which is a degree of robustness of the generated images with respects to a minute change in the input latent variables, is enhanced while the versatility as a whole is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also discuss the impact of utilizing chaotic time series in retrieving images from the trained generator.
READ FULL TEXT
Comments
There are no comments yet.