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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
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Data Provided by:
time series
Dataset Owner: N/A

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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:adam.wunderlich@nist.gov
Keywords time series , machine learning , band-limited noise , power law noise , shot noise , impulsive noise , colored noise , fractional Gaussian noise , fractional Brownian motion
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    "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": [
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    "title": "Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models",
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    "bureauCode": [
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    "modified": "2022-07-03 00:00:00",
    "publisher": {
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    "theme": [
        "Mathematics and Statistics:Image and signal processing",
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    "issued": "2022-07-08",
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        "time series",
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}

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