medical-1

Synthetic Data Generation

3260 papers • 126 benchmarks • 313 datasets

The generation of tabular data by any means possible.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in synthetic-data-generation-2

Trend

Dataset

Best Model

Actions

UCI Epileptic Seizure Recognition

Libraries

i

Use these libraries to find synthetic-data-generation-2 models and implementations

eriklindernoren/PyTorch-GAN

2 papers 15,632

Datasets

UCI Machine Learning Repository

Creative Flow+ Dataset

Synthetic COVID-19 CXR Dataset

CBTex

SICKLE

Subtasks

No subtasks available.

Most implemented papers

Wasserstein GAN

Martín Arjovsky, Soumith Chintala, Léon Bottou•Wed Jan 25 2017

This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.

5007

Content

unicredit/ganzo

2 papers 66

CtrlZ1/Domain-Adaptation-Algorithms

2 papers 41

eliabntt/GRADE-RR

2 papers 32

kynk94/TF2-Image-Generation

2 papers 27

lvyufeng/MindSpore-GAN

2 papers 22

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Improved Training of Wasserstein GANs

Aaron C. Courville, Vincent Dumoulin, Martín Arjovsky, Ishaan Gulrajani, Faruk Ahmed•Thu Mar 30 2017

This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning.

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Synthetic QA Corpora Generation with Roundtrip Consistency

Chris Alberti, Michael Collins, Jacob Devlin, D. Andor, Emily Pitler•Fri May 31 2019

A novel method of generating synthetic question answering corpora is introduced by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency, establishing a new state-of-the-art on SQuAD2 and NQ.

267 0

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Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Zinan Lin, Alankar Jain, Chen Wang, G. Fanti, Vyas Sekar•Sun Sep 29 2019

This work explores if and how generative adversarial networks can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge and designs a custom workflow called DoppelGANger, which achieves up to 43% better fidelity than baseline models.

249 0

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SinGAN-Seg: Synthetic training data generation for medical image segmentation

M. Riegler, P. Halvorsen, Steven Hicks, Vajira Lasantha Thambawita, S. Parasa, T. Lange, Pegah Salehi, Sajad Amouei Sheshkal, Hugo L.Hammer•Mon Jun 28 2021

This study presents a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image, which is different from the traditional generative adversarial networks (GANs) because the model needs only a single image and the corresponding ground truth to train.

106 0

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Generating Multidimensional Clusters With Support Lines

Nuno Fachada, Diogo de Andrade•Mon Jan 23 2023

Cugen is presented, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions, and has the potential to be a widely used framework in diverse clustering-related research tasks.

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HP-GAN: Probabilistic 3D Human Motion Prediction via GAN

Zicheng Liu, E. Barsoum, J. Kender•Sun Nov 26 2017

A novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which the model learns a probability density function of future human poses conditioned on previous poses.

362 0

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CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data

Qi Yan, Jianhao Zheng, Simon Reding, Shanci Li, I. Doytchinov•Wed Dec 15 2021

This work presents a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data, and introduces CrossLoc, a cross-modal visual representation learning approach to pose estimation that makes full use of the scene coordinate ground truth via self-supervision.

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Burst Denoising with Kernel Prediction Networks

B. Mildenhall, J. Barron, Ren Ng, Jiawen Chen, Dillon Sharlet, Robert E. Carroll•Tue Dec 05 2017

A convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima are proposed.

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Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

Chen Change Loy, Liming Jiang, Kenny T. R. Voo•Wed May 11 2022

This study proposes two occlusion generation techniques, Naturalistic Occlusion Generation (NatOcc), for producing high-quality naturalistic synthetic occluded faces; and Random Occlusions Generation (RandOcc), a more general synthetic Occluded data generation method.

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