SDGym
SDV
SDGym | SDV | |
---|---|---|
1 | 59 | |
242 | 2,141 | |
1.2% | 2.4% | |
7.8 | 9.4 | |
2 days ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
SDGym
-
[D] Synthetic data generation techniques for data privacy
I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.
SDV
-
Synthetic data generation for tabular data
Can someone help me understand the licensing of this?
https://github.com/sdv-dev/SDV/blob/main/LICENSE
It was MIT licensed up until 2022 where it was changed to what it is now, where they say that it will become MIT again 4 years after release... but is that from when the license was changed or the first release of the software in GitHub?
- SDV: NEW Data - star count:1441.0
- FLaNK Stack Weekly for 30 April 2023
- SDV: NEW Data - star count:1196.0
What are some alternatives?
Mimesis - Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
CTGAN - Conditional GAN for generating synthetic tabular data.
Copulas - A library to model multivariate data using copulas.
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.
DPL - [NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
tsfresh - Automatic extraction of relevant features from time series:
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
EigenGAN-Tensorflow - EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)
genalog - Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.