Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates that the concept of sparsity, well known especially in statistics, can provide a pathway to more efficient diffusion pipelines. Our mathematical guarantees prove that sparsity can reduce the input dimension's influence on the computational complexity to that of a much smaller intrinsic dimension of the data. Our empirical findings confirm that inducing sparsity can indeed lead to better samples at a lower cost.
RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
Department of Statistics and Data Science, Yale University（耶鲁大学统计与数据科学系）；School of Data Science and Society, University of North Carolina at Chapel Hill（北卡罗来纳大学夏洛特分校数据科学与社会学院）
An Analysis of Regularization and Fokker-Planck Residuals in Diffusion Models for Image Generation
MCIN/ AEI / 10.13039/501100011033 / FEDER, UE（MCIN/ AEI / 10.残补10.13039/501100011033 / FEDER, UE）；Autonomous Community of Madrid（马德里自治区）；ELLIS Unit Madrid（马德里ELLIS单位）；Centro de Computación Científica-Universidad Autónoma de Madrid (CCC-UAM)（马德里自治大学计算中心（CCC-UAM））
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Perceptually Regularized Diffusion Model for Image Super-Resolution
University of North Carolina（北卡罗来纳大学）；Texas Tech University（德克萨斯理工大学）；Case Western Reserve University（凯斯西储大学）；University of Houston（休斯顿大学）；University of Kentucky（肯塔基大学）
Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study
Université Claude Bernard Lyon 1（克莱尔-伯纳德里昂1大学）；Laboratoire SAF, ISFA, France（SAF实验室，ISFA，法国）；Department of Mathematics University of Vienna, Austria（数学系，维也纳大学，奥地利）；The Ohio State University, USA（俄亥俄州立大学，美国）
FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
School of Computing and Information Technology, University of Wollongong, Wollongong, Australia（新南威尔士大学计算机与信息科技学院）；School of Electrical and Computer Engineering, The University of Sydney, Sydney, Australia（悉尼大学电子与计算机工程学院）；QLD Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Australia（昆士兰大学昆士兰农业与食品创新联盟）
Make $\ell_1$ Regularization Effective in Training Sparse CNN
Pennsylvania State University（宾夕法尼亚州立大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
School of Computer Science and Technology, Tongji University, Shanghai, China（同济大学计算机科学与技术学院，上海，中国）；MOE Key Lab of Embedded System and Service Computing, Tongji University, Shanghai, China（教育部嵌入式系统与服务计算重点实验室，同济大学，上海，中国）；Shanghai Innovation Institute（上海创新研究院）
Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
The Australian National University（澳大利亚国立大学）；Rutgers University（罗格斯大学）；Data61 CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）
