Search: diffusion models

More than 1,000 papers match: here are the 1,000 most recent, ranked by relevance.

• Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation

  Ziyue Lin, Jiahe Hou, Hongyu Xia, Xinrui Xie, Feifei Wang, Yuyin Zhou, Wei Wang, Jiawei Liu, Liangqiong Qu · 2 June 2026 · Generative Adversarial Networks and Image Synthesis

  We propose Decoupled Residual Denoising Diffusion models (DRDD) for unified and data-efficient image-to-image (I2I) translation. While diffusion models have advanced I2I translation in terms of quality and diversity, we uncover a previously under-explored property in diffusion models. Crucially, bey…

• Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Synthesis with Modality and Metadata Conditioning

  Muhammad Nabi Yasinzai, Remika Mito, Mangor Pedersen · 2 June 2026 · Functional Brain Connectivity Studies

  Multimodal MRI provides complementary information for neuroimaging analysis, where different imaging modalities capture distinct anatomical, tissue, and pathological features that support the development and evaluation of downstream AI applications. Although large-scale structural MRI resources are …

• Score-Control for Hallucination Reduction in Diffusion Models

  Mahesh Bhosale, Naresh Kumar Devulapally, Abdul Wasi, Chau Pham, Vishnu Suresh Lokhande, David Doermann · 2 June 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have emerged as the backbone of modern generative AI, powering advances in vision, language, audio and other modalities. Despite their success, they suffer from hallucinations, implausible samples that lie outside the support of true data distribution, which degrade reliability and …

• Self-Regulating Annealing in Heavy-Tailed Diffusion Models

  Keito Wakatsuki, Hideaki Shimazaki · 2 June 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient, its suitability for heavy-tailed datasets remains unclear. To address this, heavy-tailed diffusion models (HTDMs) extend the standard formulation by…

• Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models

  Shreyansh Modi, Akshat Tomar, Aarush Aggarwal · 2 June 2026 · Generative Adversarial Networks and Image Synthesis

  Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We show that h-space patching, the dominant paradigm for training-free diffusion editing, systematically fails for global, low-level transformations re…

• Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

  Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla, Nuno Moniz · 2 June 2026 · Computational Drug Discovery Methods

  Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative models conditioned on cancer genotypes offer a promising avenue for personalized drug discovery, yet existing approaches la…

• Data Enrichment for Symbolic Regression Using Diffusion Models

  Simon De Reuver, Tamas Kristof Toth, Teddy Lazebnik · 2 June 2026 · Model Reduction and Neural Networks

  Symbolic regression (SR) offers a route to scientific discovery by converting observations into interpretable governing equations. However, despite its promise, its reliability degrades sharply when spatiotemporal measurements are sparse, noisy, or physically incomplete, as commonly occurring in pra…

• GLENS: Global Search via Learning from Solver Iterates with Diffusion Models

  Anjian Li, Bartolomeo Stellato, Ryne Beeson · 2 June 2026 · Advanced Optimization Algorithms Research

  We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems. The goal is for these initial guesses to be high-quality (i.e., a numerical solver converges quickly) and diverse (i.e., represent many different lo…

• You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models

  Kairan Zhao, Eleni Triantafillou, Peter Triantafillou · 2 June 2026 · Generative Adversarial Networks and Image Synthesis

  Generative models have been shown to "memorize" certain training data, leading to verbatim or near-verbatim generating images, which may cause privacy concerns or copyright infringement. We introduce Guidance Using Attractive-Repulsive Dynamics (GUARD), a novel framework for memorization mitigation …

• Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization

  Jingyun Liang, Min Wei, Shikai Li, Yizeng Han, Hangjie Yuan, Lei Sun, Weihua Chen, Fan Wang · 2 June 2026 · Human Pose and Action Recognition

  Diffusion models have shown remarkable success in video generation. However, whether such models are truly aware of the 3D structure underlying visual observations, rather than simply reproducing plausible 2D projections, remains an open question. In this work, we investigate this question through h…

• Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

  Guang Lin, Shikui Tu, Lei Xu · 2 June 2026 · Computational Drug Discovery Methods

  Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD). Existing generative approaches, however, often rely on costly post-hoc processing during Sampling or require carefully …

• DASH: Dual-Branch Score Distillation for Guidance-Calibrated Compact Diffusion Models

  Abdullah Al Shafi, Kazi Saeed Alam, Sk Imran Hossain, Engelbert Mephu Nguifo · 2 June 2026 · Model Reduction and Neural Networks

  Parameter compression of class-conditional diffusion models reveals an underexplored limitation in output-level distillation: the unconditional score branch remains unsupervised, leaving the classifier-free guidance gap underdetermined in the student. This gap, amplified at every denoising step, adm…

• Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models

  Jinyang Du, Shenghao Jin, Ziqian Xu, Ruihao Gong, Shiqiao Gu, Yang Yong, Jinyang Guo, Xianglong Liu · 2 June 2026 · Image and Video Quality Assessment

  Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint. This paper studies a deployment-oriented compression pipeline for Wan2.2-T2V-A14B by combining few-step distribution-m…

• TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models

  Yichuan Mo, Yukun Jiang, Yanbo Shi, Mingjie Li, Michael Backes, Yang Zhang, Yisen Wang · 2 June 2026 · Adversarial Robustness in Machine Learning

  The rapid development of Language Diffusion Models (LDMs) challenges the dominant position of auto-regressive competitors in language processing. However, their flexible, any-order decoding strategies not only enable fast decoding speed but also potentially bring new trustworthiness challenges. To b…

• Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations

  Victor M. Yeom-Song, Severi Rissanen, Arno Solin, Samuel Kaski, Mingfei Sun · 1 June 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have become a powerful generative prior for solutions of partial differential equations (PDEs). Existing approaches enforce physical constraints either by adding the PDE residuals as loss regularizers or through inference-time adjustments. These methods bias the model away from the …

• Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks

  Gyeonghoon Ko, Juho Lee · 1 June 2026 · Generative Adversarial Networks and Image Synthesis

  Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. However, training requires sampling from and differentiating the manifold heat kernel, which is rarely available in closed form beyond a few highly sy…

• What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation

  Qing Wang, Jacob Devasier, Chengkai Li · 1 June 2026 · Large Language Models

  We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectories -- the order in which tokens are unmasked during iterative decoding -- and find that, unlike autoregressive LLMs which generate text linearly, MDLMs…

• Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints

  Shervin Khalafi, Alejandro Ribeiro, Dongsheng Ding · 1 June 2026 · Generative Adversarial Networks and Image Synthesis

  Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conflicting objectives. We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretraine…

• Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models

  Arunkumar Kannan, Yanbo Zhang, Han Liu, Michael Baumgartner, Jianing Wang, Alexander Hertel, Bogdan Georgescu, Sasa Grbic · 1 June 2026 · Lung Cancer Diagnosis and Treatment

  While automated diagnosis systems have achieved remarkable success in computed tomography (CT)-based lung cancer screening, their development remains limited by the scarcity of diverse, annotated pulmonary nodule datasets. Diffusion-based generative models offer a promising strategy for data synthes…

• Generating Graph-like Rules for Knowledge Graph Reasoning via Diffusion Models

  Haoxiang Cheng, Yunfei Wang, Chao Chen, Kewei Cheng, Zhipeng Lin, Haoxuan Li, Changjun Fan, Shixuan Liu · 1 June 2026 · Advanced Graph Neural Networks

  Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns. However, existing rule mining methods predominantly focus on simple chain-like rules and therefore neglect the richer relational information encoded i…

• Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions

  Jingda Wu, Changxiao Cai · 29 May 2026 · Stochastic Gradient Optimization Techniques

  Score-based diffusion models have demonstrated remarkable empirical success in learning high-dimensional distributions, particularly those exhibiting low-dimensional and multi-modal structures. However, theoretical understanding of their statistical efficiency remains limited. Existing theories typi…

• Three-dimensional Conditional Diffusion Models for Cosmological 21 cm Lightcone Emulation

  Bin Xia, John H. Wise · 29 May 2026 · Radio Astronomy Observations and Technology

  We investigate conditional diffusion modeling for three-dimensional 21 cm lightcone emulation, focusing on cubes with a sky-plane size of $64\times64$ and a line-of-sight depth up to 1024 cells. Relative to earlier 2D studies, the 3D setting is substantially harder because memory limits enforce very…

• Spectral Guidance for Flexible and Efficient Control of Diffusion Models

  Gabriel Moreira, Manuel Marques, Jo\~ao Paulo Costeira, Chenyan Xiong · 29 May 2026 · Model Reduction and Neural Networks

  We introduce Spectral Guidance, a framework for controlling diffusion models by leveraging the intrinsic geometry of the generative process. As data is progressively corrupted by noise, only a small number of features remain informative for control. We characterize them as the singular functions of …

• Finding DoRI: Discovery of Retained Images in Diffusion Models

  Antoni Kowalczuk, Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch · 29 May 2026 · Digital Humanities and Scholarship

  Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potential to inadvertently memorize and replicate training data. Recent mitigation efforts have focused on identifying and pru…

• Masked Diffusion Modeling for Anomaly Detection

  Lixing Zhang, Yuchen Liang, Liyan Xie · 29 May 2026 · Anomaly Detection Techniques and Applications

  Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. However, developing effective anomaly detection methods for categorical, mixed-type, and discrete sequence data remains challenging and relatively underexpl…