U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Breadcrumb

  1. Home

Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2022-07-03 00:00:00
Date Created: N/A
Views:
Data Provided by:
Dataset Owner: N/A

Access this data

Contact dataset owner Access URL
Landing Page URL
Table representation of structured data
Title Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models
Description This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.
Modified 2022-07-03 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords time series , machine learning , band-limited noise , power law noise , shot noise , impulsive noise , colored noise , fractional Gaussian noise , fractional Brownian motion
{
    "identifier": "ark:\/88434\/mds2-2695",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Adam Wunderlich"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2695",
    "title": "Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models",
    "description": "This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "accessURL": "https:\/\/github.com\/usnistgov\/NoiseGAN",
            "format": "python source code",
            "description": "GitHub repository",
            "title": "GitHub repository"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2022-07-03 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Mathematics and Statistics:Image and signal processing",
        "Mathematics and Statistics:Modeling and simulation research"
    ],
    "keyword": [
        "time series",
        "machine learning",
        "band-limited noise",
        "power law noise",
        "shot noise",
        "impulsive noise",
        "colored noise",
        "fractional Gaussian noise",
        "fractional Brownian motion"
    ]
}

Was this page helpful?