mahmoodm2 / tableGANLinks
tableGAN is a synthetic data generation technique (Data Synthesis based on Generative Adversarial Networks paper) based on Generative Adversarial Network architecture (DCGAN).
☆152Updated 6 years ago
Alternatives and similar repositories for tableGAN
Users that are interested in tableGAN are comparing it to the libraries listed below
Sorting:
- Generative adversarial training for generating synthetic tabular data.☆295Updated 2 years ago
- We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review …☆557Updated 4 months ago
- Benchmarking synthetic data generation methods.☆283Updated last week
- A toolbox for differentially private data generation☆131Updated 2 years ago
- Evaluate real and synthetic datasets against each other☆92Updated 3 months ago
- Official git for "CTAB-GAN: Effective Table Data Synthesizing"☆92Updated last year
- Official GitHub for CTAB-GAN+☆80Updated last year
- Generating Tabular Synthetic Data using State of the Art GAN architecture☆80Updated 5 years ago
- Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs☆45Updated 2 years ago
- ☆109Updated 2 years ago
- ☆65Updated 2 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆43Updated 4 years ago
- COR-GAN: Correlation-Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records☆56Updated 4 years ago
- [IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions☆308Updated 2 years ago
- Differentially-private Wasserstein GAN implementation in PyTorch☆28Updated 6 years ago
- Source code of paper "Differentially Private Generative Adversarial Network"☆70Updated 6 years ago
- Metrics to evaluate quality and efficacy of synthetic datasets.☆251Updated this week
- Conditional GAN for generating synthetic tabular data.☆1,477Updated 2 weeks ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆34Updated 6 years ago
- [ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"☆499Updated last year
- ☆16Updated 5 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆33Updated 5 years ago
- Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)☆352Updated last month
- A novel approach for synthesizing tabular data using pretrained large language models☆325Updated last week
- Adversarial Attacks on Deep Neural Networks for Time Series Classification☆79Updated 5 years ago
- TimeSHAP explains Recurrent Neural Network predictions.☆193Updated last year
- Code for the paper "Generating Multi-Categorical Samples with Generative Adversarial Networks"☆49Updated 2 years ago
- Automating Outlier Detection via Meta-Learning (Code, API, and Contribution Instructions)☆186Updated 3 years ago
- A repo for transfer learning with deep tabular models☆104Updated 2 years ago
- An End-to-end Outlier Detection System☆257Updated 2 years ago