A curated list of resources dedicated to Synthetic Data

If you want to contribute to this list, read the contribution guidelines first. Please add your favorite synthetic data resource by raising a pull request

Also, a listed repository should be deprecated if:

• Repository's owner explicitly says that "this library is not maintained".
• Not committed for a long time (2~3 years).

• Research Summaries and Trends
• Tutorials
  • Reading Content
  • Videos and Online Courses
• Libraries
  • Text
  • Image
  • Audio
  • Simulation
• Academic Papers
  • Language Models
  • Generative Adversarial Networks (GANs)
  • Diffusion Models
  • Fair AI
  • Algorithmic Privacy
• Services
• Prominent Synthetic Data Research Labs
• Datasets

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Introductions and Guides to Synthetic Data

Blogs and Newsletters

• The Unreasonable Effectiveness of Recurrent Neural Networks - Andrej Karpathy's intro to RNNs.
• Annotated Diffusion - Tutorial on original diffusion model paper with code

Videos and Online Courses

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• Learning to Generate Data by Estimating Gradients of the Data Distribution - Video by Yang Song from Stanford. Excellent theory and interesting applications.

Open Source Generative Synthetic Data Models, Libraries and Frameworks | Back to Top

• gretel-synthetics - Generative models for structured and unstructured text, tabular, and multi-variate time-series data featuring differentially private learning.
• SDV - Synthetic Data Generator for tabular, relational, and time series data.
• Synthea - Synthetic Patient Population Simulator.
• ydata-synthetic - Synthetic structured data generators.
• synthpop - A tool for producing synthetic versions of microdata.

• Contrastive Unpaired Translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan.
• StyleGAN 3 - Official PyTorch implementation of StyleGAN3 from NeurIPS 2021.
• Denoising Diffusion Pytorch - Implementation of DDPM

• Jukebox - OpenAI's Jukebox- A Generative Model for Music.

• AirSim - AirSim is a simulator for drones, cars and more, built on Unreal and Unity engines.
• Nvidia Dataset Synthesizer - NDDS is a UE4 plugin from NVIDIA to empower computer vision researchers to export high-quality synthetic images with metadata.
• OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms.
• Unity Perception Perception toolkit for sim2real training and validation in Unity.

• Video Diffusion Pytorch - Implementation of video diffusion models in pytorch

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• Evaluating Large Language Models Trained on Code (2021) Mark Chen et al. [pdf]

• Modeling Tabular Data using Conditional GAN (2019) Xu et al. [pdf]
• Generating Long Videos of Dynamic Scenes (2022) Tim Brooks [pdf]
• Generative Adversarial Networks (2014) Ian J. Goodfellow et al. [pdf]
• Conditional Generative Adversarial Nets (2014) Mehdi Mirza et al. [pdf]
• Modeling Tabular Data using Conditional GAN (2019) Xu et al. [pdf]
• Wasserstein GAN (2017) Martin Arjovsky, et al.[pdf]
• Improved Training of Wasserstein GANs (2017) Ishaan Gulrajani, et al. [pdf]
• Time-series Generative Adversarial Networks (2019) Jinsung Yoon, et all [pdf]

• Generative Modeling by Estimating Gradients of the Data Distribution (2021) Yang Song [pdf]
• Diffusion Models are Autoencoders S. Dielman (2021) [pdf]
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) J Sohl-Dickstein et al. [pdf]
• KNN-Diffusion: Image Generation via Large-Scale Retrieval (2022) Oron Ashual [pdf]

• A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle (2021) Harini Suresh, John Guttag [pdf]
• DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks (2021) Boris van Breugel et al [pdf]
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (2021) Emily M. Bender, et al. [pdf]
• A Survey on Bias and Fairness in Machine Learning (2022) Ninareh Mehrabi [pdf]
• AI Fairness (Approaches & Mathematical Definitions) (2022) Jonathan Hui [blog]
• AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias (2018) Rachel K. E. Bellamy et al [pdf]

• Deep Learning with Differential Privacy (2016) Abadi et al. [pdf]
• An Efficient DP-SGD Mechanism for Large Scale NLP Models (2021) Dupuy et al. [pdf]
• PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018) Jordon et al. [pdf]
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence (2021) Cao et al. [pdf]
• Differentially Private Fine-tuning of Language Models (2022) Yu et al. [pdf]

Synthetic Data as API with higher level functionality such model training, fine-tuning, and generation | Back to Top

• List of Synthetic Data Startups in 2021 - Not all of these necessarily have APIs.

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• HuggingFace Datasets - Library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks.
• Google Cloud Public Datasets - Publicly available and free machine learning and analytics datasets.
• Kaggle Datasets - Data science and machine learning datasets.
• /r/datasets - A place to share, find, and discuss Datasets.
• Papers with Code - Datasets - The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.
• Awesome Public Datasets - Topic centric, high quality, public data sources
• Data.gov - U.S. Government's open data
• UC Irvine Machine Learning Repository - Popular datasets used by the machine learning community
• Google Research Dataset Search - Discover datasets hosted in thousands of repositories across the web

License - CC0