:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Shaorong, Cheng, Yuanbin, Yu, Nanpeng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2406.01913
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Exploring the Design Space of Diffusion Bridge Models
by: Zhang, Shaorong, et al.
Published: (2024)

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
by: Wang, Junfei, et al.
Published: (2025)

Self-Improving Diffusion Models with Synthetic Data
by: Alemohammad, Sina, et al.
Published: (2024)

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method
by: Liang, Xinyu, et al.
Published: (2024)

SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
by: Brockschmidt, Marie, et al.
Published: (2025)

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
by: Cheng, Yinan, et al.
Published: (2024)

Physics-informed Diffusion Generation for Geomagnetic Map Interpolation
by: Li, Wenda, et al.
Published: (2026)

DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
by: Wang, Zhixian, et al.
Published: (2023)

Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
by: Li, Dawei, et al.
Published: (2025)

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
by: Zhou, Ying, et al.
Published: (2024)

Membership Inference over Diffusion-models-based Synthetic Tabular Data
by: Cheng, Peini, et al.
Published: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)

Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models
by: Hu, Zizhao, et al.
Published: (2025)

Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models
by: Wang, Zhenyi, et al.
Published: (2023)

CasualSynth: Generating Structurally Sound Synthetic Data
by: Cheng, Zehua, et al.
Published: (2026)

A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
by: Yuan, Yefeng, et al.
Published: (2024)

Synthetic Data Generation for Augmenting Small Samples
by: Liu, Dan, et al.
Published: (2025)

Synthetic Data from Diffusion Models Improve Drug Discovery Prediction
by: Hu, Bing, et al.
Published: (2024)

Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples
by: Charte, Francisco, et al.
Published: (2025)

When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models
by: Mustaqim, S. M., et al.
Published: (2025)

ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
by: Zhang, Chenshuang, et al.
Published: (2024)

Fill In The Gaps: Model Calibration and Generalization with Synthetic Data
by: Ba, Yang, et al.
Published: (2024)

Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks
by: Alexandre, Henrique de Lima, et al.
Published: (2025)

Generating High-quality Privacy-preserving Synthetic Data
by: Yavo, David, et al.
Published: (2026)

ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
by: Rahimi, Parsa, et al.
Published: (2025)

Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
by: B, Aravind, et al.
Published: (2026)

A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula
by: Sancaktar, Cansu, et al.
Published: (2026)

Generating Synthetic Relational Tabular Data via Structural Causal Models
by: Hoppe, Frederik, et al.
Published: (2025)

Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
by: Chinodakufa, Tapiwa Amion, et al.
Published: (2026)

PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems
by: Wang, Hao, et al.
Published: (2025)

DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design
by: Wu, Bing-Yue, et al.
Published: (2025)

Enhancing Table Representations with LLM-powered Synthetic Data Generation
by: Yang, Dayu, et al.
Published: (2024)

Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation
by: Vishwakarma, Rahul, et al.
Published: (2025)

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
by: Belgodere, Brian, et al.
Published: (2023)

Generative Correlation Manifolds: Generating Synthetic Data with Preserved Higher-Order Correlations
by: d'Hondt, Jens E., et al.
Published: (2025)

User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation
by: Balog, Krisztian, et al.
Published: (2025)

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
by: Takahashi, Hiroshi, et al.
Published: (2025)

Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
by: Huang, Chun-Yin, et al.
Published: (2024)

CodecLM: Aligning Language Models with Tailored Synthetic Data
by: Wang, Zifeng, et al.
Published: (2024)

High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
by: Huang, Zhuoyi, et al.
Published: (2025)