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

    Metadata Updated: July 29, 2022

    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.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: See this page for license information.

    Downloads & Resources

    Dates

    Metadata Created Date July 29, 2022
    Metadata Updated Date July 29, 2022

    Metadata Source

    Harvested from NIST

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date July 29, 2022
    Metadata Updated Date July 29, 2022
    Publisher National Institute of Standards and Technology
    Maintainer
    Identifier ark:/88434/mds2-2695
    Data First Published 2022-07-08
    Language en
    Data Last Modified 2022-07-03 00:00:00
    Category Mathematics and Statistics:Image and signal processing, Mathematics and Statistics:Modeling and simulation research
    Public Access Level public
    Bureau Code 006:55
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 5ef267ad-807b-4bf7-9f93-f561f9782861
    Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
    Harvest Source Title NIST
    Homepage URL https://data.nist.gov/od/id/mds2-2695
    License https://www.nist.gov/open/license
    Program Code 006:045
    Source Datajson Identifier True
    Source Hash f26a9a31cea845c79448e17c801b5f51d289d789
    Source Schema Version 1.1

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