Synthetic Data Generation
64 papers with code • 1 benchmarks • 3 datasets
The generation of tabular data by any means possible.
Libraries
Use these libraries to find Synthetic Data Generation models and implementationsMost implemented papers
Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
HP-GAN: Probabilistic 3D human motion prediction via GAN
Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a handheld camera.
Synthetic QA Corpora Generation with Roundtrip Consistency
We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.
CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation
Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task.
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.