Búsqueda: diffusion models

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• Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?

  Marta Aparicio Rodriguez, Anastasia Borovykh, Grigorios A. Pavliotis, Daniel J. Korchinski · 1 de junio de 2026 · Generative Adversarial Networks and Image Synthesis

  Generative models have a persistent limitation: their tendency to memorize training data can create legal liabilities and erode creative diversity. Understanding which samples are memorized in whole or in part, and under what conditions, therefore remains an important open problem. Here we answer th…

• Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

  Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra · 27 de agosto de 2026 · Medical Image Segmentation Techniques

  Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local texture patterns and lack awareness of global anatomical structures, leading to boundary delineation failures in low-data regimes. This research pape…

• CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models

  Ji Guo, Xiaolong Qin, Cencen Liu, Jielei Wang, Jierun Chen, Wenbo Jiang · 6 de mayo de 2026 · Multimodal Machine Learning Applications

  Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as their applications become increasingly widespread, recent studies have revealed that VLMs are vulnerable to backdoor attacks. Existing backdoor attacks on…

• Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training

  Hanna Malet, Gabriel Turinici · 2 de octubre de 2026 · Reservoir Engineering and Simulation Methods

  Model collapse arises when generative models are trained on synthetic data produced by earlier models. The phenomenon has attracted considerable attention because of its societal and technical implications. However, previous studies have reached seemingly contradictory conclusions: replacing real da…

• Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning

  Antoine Lorentz, St\'ephane May, Valentine Bellet, Dawa Derksen, Bastien Nespoulous · 28 de septiembre de 2026 · Remote Sensing and LiDAR Applications

  Large-scale Digital Surface Models (DSMs) can be produced cost-effectively from satellite images via stereo-photogrammetry. However, the resulting 3D maps are often contaminated by noise, outliers, and voids. On the other hand, aerial LiDAR provides high-accuracy elevation measurements at a substant…

• TINA+: Probing Residual Visual Knowledge in Unlearned Diffusion Models via Diffusion-Consistent Text-Free Inversion

  Qianlong Xiang, Miao Zhang, Kun Wang, Haoyu Zhang, Junhui Hou, Liqiang Nie · 19 de agosto de 2026 · Generative Adversarial Networks and Image Synthesis

  Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for preventing harmful content. Existing adversarial probes evaluate these methods by testing whether erased concepts can still be recovered. However, existing erasure and probe meth…

• Quantifying Error Propagation and Model Collapse in Diffusion Models

  Nail B. Khelifa, Richard E. Turner, Ramji Venkataramanan · 1 de junio de 2026 · Stochastic Gradient Optimization Techniques

  Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to significantly degrade performance in a wide range of tasks, often characterized by a progressive drift away from the target distribution. In this work, we theoreti…

• HPSv3++: Scaling Reward Models Across the Full Spectrum of Diffusion Model Capabilities

  Yijun Liu, Jie Huang, Zeyue Xue, Yuming Li, Ruizhe He, Haoran Li, Shijia Ge, Siming Fu · 15 de junio de 2026 · Multimodal Machine Learning Applications

  Reward models guide text-to-image (T2I) systems toward outputs aligned with human preferences. However, typical reward models such as HPSv3 are trained on pre-annotated data from earlier T2I models, without accounting for quality discriminative shifts arising from evolving model capabilities and rei…

• VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models

  Tianci Bi, Xiaoyi Zhang, Yan Lu, Nanning Zheng · 24 de abril de 2026 · Generative Adversarial Networks and Image Synthesis

  The performance of Latent Diffusion Models (LDMs) is critically dependent on the quality of their visual tokenizers. While recent works have explored incorporating Vision Foundation Models (VFMs) into the tokenizers training via distillation, we empirically find this approach inevitably weakens the …

• CoRe: Co-Evolving Reward Models for Mitigating Latent Reward Hacking in Video Diffusion Models

  Zhaolong Su, Yujin Han, Feng Wang, Jameson Dong, Hins Hu, Difan Zou · 30 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Latent reward models (LRMs) enable efficient alignment of video diffusion models by scoring intermediate states directly in latent space. However, we find that optimizing against a fixed latent reward rapidly leads to latent reward hacking: the predicted reward stays high while perceptual and motion…

• Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing

  Leyi Qi, Yiming Li, Siyuan Liang, Zhengzhong Tu, Dacheng Tao · 29 de mayo de 2026 · Adversarial Robustness in Machine Learning

  Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intellectual property concerns, making model ownership verification (MOV) increasingly critical. We find that existing backdoor-based diffusion watermarkin…

• Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

  Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang · 27 de abril de 2026 · Functional Brain Connectivity Studies

  Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most available longitudinal neuroimaging datasets are temporally sparse with a f…

• Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

  Yongheng Sun, Minhui Yu, Mengqi Wu, Maureen Kohi, Mingxia Liu · 23 de junio de 2026 · Generative Adversarial Networks and Image Synthesis

  While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical utility is constrained by high operational costs and cumulative radiation risks. Recent deep generative models show promise in longitudinal image synthes…

• IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models

  Vladislav Gromadskii, David Li, Samson Gourevitch, Yazid Janati, Eric Moulines, Maxim Panov, Alexander Korotin · 5 de octubre de 2026 · Generative Adversarial Networks and Image Synthesis

  Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable sequence likelihoods complicate reward fine-tuning. We introduce IDRF, a framework for reward fine-tuning of few-step masked discrete diffusion generator…

• Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming

  Ziwen Liu, Yan Liu, Congying Han, Tiande Guo, Yao Yan, Weichen Zhao · 5 de octubre de 2026 · Risk and Portfolio Optimization

  Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditional and learning-based approaches, optimizing non-convex or non-smooth objectives and adapting to different objectives under fixed chance constrain…

• Controlling Polar Exposure to Delay Memorization in Diffusion Models

  Xuanchen Wang, Heng Wang, Weidong Cai · 5 de octubre de 2026 · Reservoir Engineering and Simulation Methods

  Diffusion models can reach useful sample quality before copying training examples, but fast optimization can compress this generalization window by accelerating sample-specific fitting. We investigate this effect through update geometry and propose Quality-Gated De-whitening (QGD), a controller that…

• Jumping up and down: Denoiser diffusion models for discrete ordinal data

  Yair Shenfeld, Ricardo Baptista, Stefano Peluchetti · 5 de octubre de 2026 · Advanced Mathematical Modeling in Engineering

  Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion models for categorical data, specifically in the language domain. In contrast, diffusion models for discrete integer-valued data are less developed, …

• Does Physics Live in the Activations? Localizing Physical Quantities in Video Diffusion Models

  Jonas Kneifl, Jakub Skalski, Bart{\l}omiej Twardowski, Kamil Deja · 5 de octubre de 2026 · Generative Adversarial Networks and Image Synthesis

  Video generation models produce strikingly realistic sequences and are increasingly proposed as world models, yet recent benchmarks reveal pronounced deficits in their physical reasoning. This raises the question of whether these models internalize physical principles or merely reproduce familiar mo…

• Parasitic Co-Denoising: Unlocking 3D Human Motion Generation in a Frozen Video Diffusion Model

  Yunjiao Zhou, Junlang Qian, Lihua Xie, Jianfei Yang · 5 de octubre de 2026 · Human Motion and Animation

  Despite never being supervised on explicit 3D motion, large-scale text-to-video diffusion models synthesize realistic human motion in their generated videos. We ask whether this implicit knowledge can be turned into explicit 3D motion generation, without training a separate motion model. Probing a f…

• VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models

  Zhen Xing, Shuyuan Tu, Qi Dai, Zihao Zhang, Hui Zhang, Han Hu, Zuxuan Wu, Yu-Gang Jiang · 5 de octubre de 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to provided text descriptions. However, most existing approaches only focus on video editing for short clips and rely on time-…

• Large Language Continuous Diffusion Models

  Zhihan Yang, Wei Guo, Jean-Marie Lemercier, Simon Welker, Yonggan Fu, Mohammad Mahdi Kamani, Sajad Norouzi, Julius Berner, Tomas Geffner, Karsten Kreis, Yongxin Chen, Molei Tao, John Thickstun, Pavlo Molchanov, Ante Juki\'c, Arash Vahdat, Morteza Mardani · 5 de octubre de 2026 · Large Language Models

  Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steera…

• Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data

  Saptarshi Chakraborty, Quentin Berthet, Peter L. Bartlett · 5 de octubre de 2026 · Reservoir Engineering and Simulation Methods

  Despite the remarkable empirical success of flow-matching models, their statistical generalization guarantees remain underdeveloped. Existing analyses often impose restrictive assumptions on the estimated velocity field and yield convergence rates that fail to reflect the intrinsic low-dimensional s…

• GFD-OPD: Guidance-Folded On-Policy Distillation of Diffusion Models Across Scales

  Zhenxing Zhang, Jiayan Teng, Wenxu Wu, Zhuoyi Yang, Jiazheng Xu, Wendi Zheng, Jie Tang, Dan Guo, Meng Wang · 1 de octubre de 2026 · Reservoir Engineering and Simulation Methods

  On-policy distillation (OPD) has demonstrated two important capabilities in language models: compressing large teachers into smaller students and merging expert models into a single model. Existing diffusion OPD, however, mostly focus on the latter, with teachers and students sharing the same backbo…

• From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Models

  Cristina L\'opez Amado, Marco Fumero, Francesco Locatello · 1 de octubre de 2026 · Advanced Mathematical Modeling in Engineering

  Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model defines a family of deterministic dynamical systems indexed by noise scale. At each fixed scale $\sigma$, we treat the denoiser as a self-map and study…

• CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models

  Shu Yu, Chaochao Lu · 1 de octubre de 2026 · Generative Adversarial Networks and Image Synthesis

  Online reinforcement learning has been extended to flow matching for diffusion model (DM) image generation. However, this paradigm faces three limitations: (1) Window selection. Existing methods manually set the stochastic differential equation (SDE) sampling window, i.e., the denoising steps where …