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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.

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

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date July 29, 2022
Metadata Updated Date July 29, 2022
Publisher National Institute of Standards and Technology
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
Schema Version
Catalog Describedby
Harvest Object Id 5ef267ad-807b-4bf7-9f93-f561f9782861
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL
Program Code 006:045
Source Datajson Identifier True
Source Hash f26a9a31cea845c79448e17c801b5f51d289d789
Source Schema Version 1.1

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