Popular GitHub Repositories for Data Generation

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

The Declarative Data Generator

Conditional GAN for generating synthetic tabular data.

Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.

A novel approach for synthesizing tabular data using pretrained large language models

A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.

Synthetic Data Generation for mixed-type, multivariate time series.

(ECCV 2024) Empowering Multimodal Large Language Model as a Powerful Data Generator

?? Official implementation of "Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition"

A command line tool for extracting machine learning ready data from software binaries powered by Radare2

End-to-end robot control based on generative diffusion model

[ACM MM 2024] Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives

? Model-Driven test data generation platform enabling developers to create realistic, scalable, and privacy-compliant test data. Features model-driven data generation, GDPR compliance, and seamless Python integration.

Synthetic data generation for TODs

Code for paper "ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models"

Python script and Lua extension using BeamNG.tech to generate low impact crash scenarios and ground truth data for imitation learning.

[MICCAI 2024] PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI

SimBEV is a configurable and scalable synthetic driving data generation tool based on the CARLA Simulator.

Code for ECML-PKDD 2022 Paper --- CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection

Synthetic financial time series generation with regime clustering