Diffusion-Based Image Generation

Follow this topic Ask about this topic

Momentum

15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 189

All topics

Ambient Dataloops: Generative Models for Dataset Refinement
Jan 21, 2026Adrián Rodríguez-Muñoz, William Daspit, Adam Klivans +3Synthetic Data Augmentation Diffusion-Based Image Generation
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology · Department of Computer Science, University of Texas at Austin
Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method
Jan 14, 2026Jackie Alex, Justin PetterSynthetic Data Augmentation Image Generation
St. Petersburg College
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Jan 13, 2026Mohamad Koohi-Moghadam, Mohammad-Ali Nikouei Mahani, Rex K. H. Au-Yeung +6Computational Pathology Synthetic Data Augmentation
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China · Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China · Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China. +2
MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
Dec 22, 2025Hui Li, Fu-Yun Wang, Haoyuan Xia +4Diffusion Model Fine-Tuning Diffusion-Based Image Generation
Shanghai Innovation Institute · Fudan University · Shanghai Academy of AI for Science +2
EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance
Dec 19, 2025Ankit Yadav, Ta Duc Huy, Lingqiao LiuDiffusion Model Guidance Classifier-Free Guidance
Australian Institute for Machine Learning, Adelaide University, Australia
Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky
Dec 5, 2025Shilin Hu, Jingyi Xu, Akshat Dave +2Conditional Image Generation Diffusion-Based Image Generation
Stony Brook University, Stony Brook, NY 11794, USA · UNC Charlotte, Charlotte, NC 28223, USA
StyleFusion360: View-Consistent Head Stylization via Adaptive Style Modulation
Nov 27, 2025Furkan Guzelant, Arda Goktogan, Tarık Kaya +1Image Style Transfer Diffusion-Based Image Generation
Bilkent University, Department of Computer Engineering, Ankara, Türkiye
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution
Nov 21, 2025Chaoran Xu, Chengkan Lv, Qiyu Chen +3Diffusion-Based Image Generation Synthetic Anomaly Generation
Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · CASI Vision Technology CO., LTD., Luoyang, China +1
Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model
Jul 11, 2025Fang Chen, Alex Villa, Gongbo Liang +3Diffusion Model Alignment Conditional Image Generation
University of California Merced · Texas A&M University-San Antonio · Oregon State University +1

Jan 21, 2026, cs.LGJ/K move · Enter open · S save

Ambient Dataloops: Generative Models for Dataset Refinement

Adrián Rodríguez-Muñoz, William Daspit, Adam Klivans, Antonio Torralba+2

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology · Department of Computer Science, University of Texas at Austin

Synthetic Data Augmentation Diffusion-Based Image Generation Diffusion Models

Open paper AskPDF arXiv

We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets contain samples of highly varying quality, and training directly on such heterogeneous data often yields suboptimal models. We propose a dataset-model co-evolution process; at each iteration of our method, the dataset becomes progressively higher quality, and the model improves accordingly. To avoid destructive self-consuming loops, at each generation, we treat the synthetically improved samples as noisy, but at a slightly lower noisy level than the previous iteration, and we use Ambient Diffusion techniques for learning under corruption. Empirically, Ambient Dataloops achieve state-of-the-art performance in unconditional and text-conditional image generation and de novo protein design. We further provide a theoretical justification for the proposed framework that captures the benefits of the data looping procedure.

Momentum

Related topics

Latest papers 189