DPL VS SDGym

Compare DPL vs SDGym and see what are their differences.

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DPL SDGym
1 1
12 243
- 1.6%
5.6 7.8
6 months ago 9 days ago
Python Python
Apache License 2.0 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.

DPL

Posts with mentions or reviews of DPL. 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.
  • [D] Synthetic data generation techniques for data privacy
    1 project | /r/MachineLearning | 15 Feb 2022
    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 DPL and SDGym you can also consider the following projects:

prompttools - Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

Mimesis - Mimesis is a robust data generator for Python that can produce a wide range of fake data in multiple languages.

deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

SDV - Synthetic data generation for tabular data

tapnet - Tracking Any Point (TAP)

Copulas - A library to model multivariate data using copulas.

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]

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.

AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.