Búsqueda: diffusion models

Más de 1000 artículos coinciden: estos son los 1000 más recientes, ordenados por relevancia.

• Rethinking Streaming Video Diffusion Model: Context, Execution, and Training

  Hongchen Zhang (University of Chinese Academy of Sciences) · 22 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Understanding the design space of streaming video diffusion is essential to exploring its potential for generation quality and computational efficiency. We develop a unified analytical framework that relates model and sampler choices, historical conditioning, execution scheduling, and training strat…

• CleanVideo: Adaptive Concept Erasure for Text-to-Video Diffusion Models

  Junchi Liao, Hongji Li, Wenrui Zhou, Lijie Hu · 18 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Concept erasure aims to selectively eliminate undesired visual semantics from pre-trained generative models without compromising their general utility. Extending concept erasure from images to video is nontrivial. Target concepts emerge gradually and vary across frames and denoising steps. As a resu…

• DART: Distillation-Aware Reparameterization for Training-Free LoRA Reuse in Few-Step Video Diffusion Models

  Shihong Li, Juntao Xu, JinCao, Maowen Tang, Jun Huang, Jintao Li · 18 de septiembre de 2026 · Reservoir Engineering and Simulation Methods

  Step distillation reduces the cost of video generation, but reusing a LoRA trained for a longer trajectory can alter its functional effect or degrade target quality. Static parameter compatibility offers one perspective on this problem; our observations show that similar measured geometry can coexis…

• Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models

  Kuluhan Binici, Cihan Acar, Shivam Aggarwal, Siying Liu, Tulika Mitra · 16 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Text-to-image diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of steps depends on the complexity of the input text prompt. We propose an adaptive diffusion controller that dynamically adjusts the number of steps to gen…

• Certifying Concept Unlearning in Text-to-Image Diffusion Models

  Mansi, Luca Marzari, Francesco Leofante · 14 de septiembre de 2026 · Adversarial Robustness in Machine Learning

  Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adversarial prompt search. However, these metrics provide only empirical evidence over a finite set of queries and leave residual leakage over the broa…

• GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models

  Qinghui Gong, Yihuai Liang, Yuanlun Xie, Deepak Kumar Jain, Vitomir \v{S}truc, Zhengchun Zhou · 14 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Text-to-image (T2I) diffusion models inevitably internalize sensitive or non-compliant concepts from large-scale pretraining data, necessitating post-hoc concept erasure. However, existing erasure methods often lack explicit constraints on parameter updates, leading to over-intervention and unintend…

• Representation-based Masked Diffusion Model

  Yangrong Hu, Ding Huang, Xueyu Zhou, Jian Huang · 14 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Masked Diffusion Models (MDMs) have emerged as a compelling paradigm for language modeling, offering the capability for efficient parallel text generation. However, existing parallel sampling methods typically update multiple masked tokens independently and ignore the complex mutual dependencies amo…

• Diffusion Models and Concept Formation

  Zekun Wang, Karthik Singaravadivelan, Christopher J. MacLellan · 14 de septiembre de 2026 · Advanced Mathematical Modeling in Engineering

  Humans organize knowledge into a taxonomy of concepts with nested levels of abstraction and a \emph{basic level} at which people recognize and name objects with the least cognitive effort. Cobweb is a classic cognitive account of this ability, an incremental learner that builds a probabilistic conce…

• Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

  Qi Chen, Jierui Zhu, Florian Shkurti · 11 de septiembre de 2026 · Energy Load and Power Forecasting

  Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically underexplored, especially lacking a full consideration of the shared encoder-generator structure. Leveraging recent information-theoretic tools, we prop…

• Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

  Iman Khazrak, Narges Nejad, Mostafa M. Rezaee, Robert C. Green II · 11 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Net with ranks {2,4,8,16,32}, fixed optimization settings, and a reproducible local-folder pytorch-fid protocol. We report FID, trainable parameters, ru…

• Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks

  Jialu Guo, Xiao Han, Junjie Wu · 11 de septiembre de 2026 · Privacy-Preserving Technologies in Data

  Diffusion models have achieved remarkable success in generative tasks across various areas, however their training process raises significant privacy concerns, particularly under membership inference attacks (MIAs). Prior studies on privacy-preserving of diffusion models fail to balance privacy, uti…

• RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation

  Ramiro Valdes Jara, David Chapman, Adam Meyers · 11 de septiembre de 2026 · Advanced Neuroimaging Techniques and Applications

  Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables. This problem is central to real-world applications such as healthcare monitoring, traffic networks, and energy systems. Recent diffusion-based approaches have sho…

• Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models

  Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan, Alison Q. O'Neil, Sotirios A. Tsaftaris · 10 de septiembre de 2026 · COVID-19 diagnosis using AI

  Latent Diffusion Models have shown remarkable results in text-guided image synthesis in recent years. In the domain of natural (RGB) images, recent works have shown that such models can be adapted to various vision-language downstream tasks with little to no supervision involved. On the contrary, te…

• Efficient Fairness Auditing Across Guidance Scales in Text-to-Image Diffusion Models via Causal Abstraction

  Nabila Tasfiha Rahman, Rajatsubhra Chakraborty, Depeng Xu, Lu Zhang · 10 de septiembre de 2026 · Bayesian Modeling and Causal Inference

  Fairness auditing of text-to-image diffusion models often requires generating large numbers of images across sampling configurations, making comprehensive evaluation computationally expensive. We propose a causal-abstraction-based audit instrument for efficiently evaluating fairness under interventi…

• Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images

  Yanxing Liu, Jiancheng Pan, Jianwei Yang, Tiancheng Chen, Peiling Zhou, Bingchen Zhang · 10 de septiembre de 2026 · Domain Adaptation and Few-Shot Learning

  Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is particularly prominent in numerous remote sensing applications such as endangered species monitoring and disaster assessment. Existing FSOD methods f…

• Interpreting Object-Dependent Concept Brittleness in Text-to-Image Diffusion Models

  Yifan Yuan, Xiangyu Liu, Hongming Shan, Yu Han, Yu Jiang, Hao Tan, Junping Zhang, Linlin Shen · 10 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Although text-to-image diffusion models generally exhibit strong prompt-following ability, we identify a persistent and previously underexplored failure pattern in which a small subset of prompts differing only in the object consistently fails to realize the same target concept under identical gener…

• SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration

  Renye Yan, Jikang Cheng, You Wu, Bojin Huang, Wei Peng, Zongwei Wang, Ling Liang, Yimao Cai · 9 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. This led to training-free methods that apply objective-guided terms in sampling to bias the generation distribution toward…

• Diffusion models for eye-gaze trajectory generation using position and velocity representations

  Laxman Basnet, Alexander Szorkovszky, Pedro G. Lind, Anis Yazidi, Shailendra Bhandari · 9 de septiembre de 2026 · Gaze Tracking and Assistive Technology

  Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of privacy constraints. We address this using two complementary denoising diffusion probabilistic models (DDPMs) for unconditional generation of eye-gaze dy…

• Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction

  Sihwa Park · 7 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By physically manipulating the TV's antenna, audiences control the clarity of AI-generated images and sounds, metaphorically enacting the denoising process…

• One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation

  Arka Pal, Rajesh Kumar, Hannes Eriksson, R\'emi Lacombe, Arvid Laveno Ling, Ankit Gupta, Maciej Wozniak · 7 de septiembre de 2026 · Autonomous Vehicle Technology and Safety

  Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion traffic model can serve two complementary roles in the autonomous driving development loop: as an ego motion planner, a…

• SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting

  Hiep V. Dang, Antonios Mamalakis · 4 de septiembre de 2026 · Meteorological Phenomena and Simulations

  Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost of operational systems, which constrains simulation fidelity. We introduce SimCas…

• Conditioning Degenerate Diffusion Models

  U\u{g}ur Ayd{\i}n, Tamer Ba\c{s}ar · 4 de septiembre de 2026 · Advanced Mathematical Modeling in Engineering

  Current conditioned generative models heavily rely on score functions for guidance during training. When the generative model is a diffusion process with a singular diffusion coefficient and the underlying (conditional) densities either do not exist or are not smooth, we use causal optimal transport…

• ToPO: Token-Conditioned Preference Routing for Attention-Based Latent Diffusion Models

  Juntao Xu, Shihong Li, Hoi Fan Au, Ning Zhu · 4 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Pairwise preference labels rank complete images, yet Diffusion-DPO applies their effect over many spatial and denoising-time coordinates. For attention-based, noise-prediction latent diffusion, ToPO (Token-Oriented Preference Optimization) constructs a per-minibatch, detached, separable spatial-temp…

• EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders

  Xinghao Wang, Dong Li, Wei Yu, Yingwei Pan, Tao Gong, Qi Chu, Nenghai Yu, Ting Yao · 4 de septiembre de 2026 · Generative Adversarial Networks and Image Synthesis

  Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated training data raises pressing safety and copyright concerns. Concept erasure offers a principled remedy by removing unwanted semantics from pretrained m…

• PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation

  Jiechao Gao, Yuandong Pan, Jie Wang, Michael Lepech, Bradford Campbell · 4 de septiembre de 2026 · Privacy-Preserving Technologies in Data

  Sensor-intensive environments enable many intelligent services by inferring user applications from heterogeneous data streams. However, not all applications should be exposed: users want some activities to stay private. This creates a tension between inferring applications for useful services and pr…