Synthetic Data Generation
180 papers with code • 1 benchmarks • 5 datasets
The generation of tabular data by any means possible.
Libraries
Use these libraries to find Synthetic Data Generation models and implementationsDatasets
Most implemented papers
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications.
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
This approach of image scraping and selection relaxes the need for a real-world domain-specific dataset that must be either publicly available or created for this purpose.
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.
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.
SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning.
Exploring Transformer Text Generation for Medical Dataset Augmentation
Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks
MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind.
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.