Synthetic Data Generation
115 papers with code • 1 benchmarks • 3 datasets
The generation of tabular data by any means possible.
Benchmarks
These leaderboards are used to track progress in Synthetic Data Generation
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Libraries
Use these libraries to find Synthetic Data Generation models and implementations
UCI Machine Learning Repository
Synthetic COVID-19 CXR Dataset
Most implemented papers
Wasserstein GAN
labmlai/annotated_deep_learning_paper_implementations
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We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Improved Training of Wasserstein GANs
igul222/improved_wgan_training
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NeurIPS 2017
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Synthetic QA Corpora Generation with Roundtrip Consistency
patil-suraj/question_generation
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ACL 2019
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.
Generating Multidimensional Clusters With Support Lines
Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for a more complete coverage of a given problem's space.
HP-GAN: Probabilistic 3D human motion prediction via GAN
ebarsoum/hpgan
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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
google/burst-denoising
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CVPR 2018
We present a technique for jointly denoising bursts of images taken from a handheld camera.
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
topo-epfl/crossloc
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CVPR 2022
We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data.
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
kennyvoo/face_occlusion_generation
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This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications.
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets.
