DoppelGANger VS SDV

Compare DoppelGANger vs SDV and see what are their differences.

DoppelGANger

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions (by fjxmlzn)
dataset-generation Privacy time-series Timeseries Gan Gans generative-adversarial-network synthetic-data synthetic-dataset-generation synthetic-data-generation synthetic-data-generator doppelganger fidelity Datasets
Source Code
arxiv.org
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SDV

Synthetic data generation for tabular data (by sdv-dev)
synthetic-data Machine Learning relational-datasets multi-table time-series synthetic-data-generation sdv data-generation generative-adversarial-network Gan Gans Deep Learning generative-ai generative-model generativeai
Source Code
docs.sdv.dev
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DoppelGANger SDV
14 59
275 2,141
- 14.0%
3.8 9.4
6 months ago 1 day ago
Python Python
BSD 3-clause Clear License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

DoppelGANger

Posts with mentions or reviews of DoppelGANger. We have used some of these posts to build our list of alternatives and similar projects.

SDV

Posts with mentions or reviews of SDV. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-27.

What are some alternatives?

When comparing DoppelGANger and SDV you can also consider the following projects:

CelebV-HQ - [ECCV 2022] CelebV-HQ: A Large-Scale Video Facial Attributes Dataset

CTGAN - Conditional GAN for generating synthetic tabular data.

tdk-demo - This is a collection of TDK demo projects that use different databases and options

gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.

FAST-RIR - This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.

gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.

tsfresh - Automatic extraction of relevant features from time series:

ydata-synthetic - Synthetic data generators for tabular and time-series data

Copulas - A library to model multivariate data using copulas.

TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python

EigenGAN-Tensorflow - EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)

DoppelGANger vs CelebV-HQ SDV vs CTGAN DoppelGANger vs tdk-demo SDV vs gretel-python-client DoppelGANger vs FAST-RIR SDV vs machine-learning-for-trading DoppelGANger vs gretel-synthetics SDV vs tsfresh DoppelGANger vs ydata-synthetic SDV vs Copulas SDV vs TimeSynth SDV vs EigenGAN-Tensorflow