DeepEcho
Synthetic Data Generation for mixed-type, multivariate time series. (by sdv-dev)
SDGym
Benchmarking synthetic data generation methods. (by sdv-dev)
| DeepEcho | SDGym | |
|---|---|---|
| 1 | 1 | |
| 88 | 243 | |
| - | 1.6% | |
| 7.6 | 7.8 | |
| 4 days ago | 9 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | 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.
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.
DeepEcho
Posts with mentions or reviews of DeepEcho.
We have used some of these posts to build our list of alternatives
and similar projects.
SDGym
Posts with mentions or reviews of SDGym.
We have used some of these posts to build our list of alternatives
and similar projects.
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[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.
What are some alternatives?
When comparing DeepEcho and SDGym you can also consider the following projects:
Mimesis - Mimesis is a robust data generator for Python that can produce a wide range of fake data in multiple languages.
SDV - Synthetic data generation for tabular data
Copulas - A library to model multivariate data using copulas.
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.
DPL - [NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.