Synthetic data generation for tabular data
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Updated
Jun 4, 2024 - Python
Synthetic data generation for tabular data
A library to model multivariate data using copulas.
Synthetic Data Generation for mixed-type, multivariate time series.
Conditional GAN for generating synthetic tabular data.
Few-shot satellite image classification for bringing deep learning on board OPS-SAT
Implementation for the paper: "Privacy-preserving data release leveraging optimal transport and particle gradient descent"
(Still not complete!!) This UI serves as a Synthetic ASR Dataset Generator powered by/for OpenAI Whisper, enabling users to capture audio, transcribing it, on the fly and manage the generated dataset. It provides a user-friendly interface for configuring audio parameters, transcription options, and dataset management.
TNO PET Lab - Synthetic Data Generation (SDG) - Tabular - Evaluation - Utility Metrics
code for the paper "GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?"
A toolset to test data classification engines that generates mock data in various file formats, sizes and data profiles.
Official pytorch implementation codes for NeurIPS-2023 accepted paper "Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation"
temporal + cnn vision model for classification of windmill defects, with unreal-engine data generation and a custom data augmentation suite
[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Implementations of various synthesizers with pytorch.
3D image registration training framework using adaptive loss weighting and synthetic data generation
(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare
A python module to generate synthetic images from 3D models, for use in image detection/segmentation tasks.
TNO PET Lab - Synthetic Data Generation (SDG) - Graph - Generation - GraphBin
Generates midi notes to join two different midi snippets by interpolation with a cubic spline curve
In this repository, I tried to investigate the utility of synthetic data generated by DataSynthesizer and Synthetic Data Vault in machine learning tasks. I applied the Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Naive Bayes algorithms to the synthetic data and made a comparison.
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