Latest Articles for Data Generation

Examining the benefits of Diffusion Models for image classification and segmentation tasks.

Explore the workings and applications of GANs in various fields.

A novel generator improves image creation across various categories using optimal transport methods.

DiffuGen efficiently generates high-quality labeled image datasets for machine learning.

A new method trains models using generated images without human labels.

New models generate realistic data across various fields and applications.

Combining GANs and VAEs for better image generation.

Explore the impact of generative diffusion models in various fields, especially network optimization.

This article discusses using diffusion models with discrete data through the probability simplex.

A new method boosts the data generation capabilities of Variational Autoencoders.

Generating realistic synthetic fingerprints for improved forensic investigations.

Synthetic data generation improves machine learning for atomic-level material analysis.

A new method for generating synthetic preference data enhances reward models in reinforcement learning.

A new method generates 3D data on human-object interactions for AI.

New method generates unseen quantum states through merging classical and quantum techniques.

OmniSCV tool creates high-quality omnidirectional images for better algorithm training.

VFMs offer faster and efficient sampling for data across various applications.

A new method aims to enhance output quality in generative models.

Leveraging LLMs to create vast datasets for intent prediction in conversation systems.

A clear breakdown of DDPMs and their practical applications in data generation.

KANDY creates structured tasks to assess AI learning and reasoning abilities.

Study evaluates LLMs' ability to create culturally relevant question-answer data.

Learn how score-based generative models create new data from noise.

This research focuses on creating realistic satellite images using semantic layouts.

This article explores a method to stabilize generative models using synthetic data.

ActGen generates relevant images to enhance model performance efficiently.

Researchers are using synthetic data to improve human pose estimation.

This study explores the theoretical underpinnings of discrete-time diffusion models in machine learning.

ObjectDR generates paired data to improve 3D shape reconstruction from 2D images.

Introducing a simpler way to train implicit generative models using Invariant Statistical Loss.

A method to enhance diffusion models for generating high-quality data efficiently.

GOLD offers a framework for generating diverse training data for small language models.

New techniques enhance the efficiency of diffusion models for generative tasks.

A novel method for improving multi-label classification using synthetic image generation.

LidarDM generates realistic LiDAR videos for autonomous vehicle training and testing.

Generative diffusion models create new data by transforming noise into realistic outputs.

A new method to improve efficiency in data generation using diffusion models.

This method combines diffusion models and GANs to generate high-quality data samples effectively.

Explore the workings and applications of diffusion models in data generation.

A new approach improves dataset synthesis for language models, enhancing output diversity.