Results 11 to 20 of about 572,440 (170)
CoRR, 2023 Diffusion models have shown remarkable performance on many generative tasks. Despite recent success, most diffusion models are restricted in that they only allow linear transformation of the data distribution. In contrast, broader family of transformations can potentially help train generative distributions more efficiently, simplifying the reverse ...
Bartosh, Grigory +2 more
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A Study on Generative Models for Visual Recognition of Unknown Scenes Using a Textual Description
Sensors, 2023 In this study, we investigate the application of generative models to assist artificial agents, such as delivery drones or service robots, in visualising unfamiliar destinations solely based on textual descriptions.
Jose Martinez-Carranza +4 more
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CoRR, 2023 Score-based diffusion models learn to reverse a stochastic differential equation that maps data to noise. However, for complex tasks, numerical error can compound and result in highly unnatural samples. Previous work mitigates this drift with thresholding, which projects to the natural data domain (such as pixel space for images) after each diffusion ...
Aaron Lou, Stefano Ermon
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Modelling Diffusion in Silicides
Le Journal de Physique Colloques, 1988 This paper outlines models for the redistribution of dopant during the incorporation of silicides in VLSI processes.
Moynagh, P., Brown, A., Rosser, P.
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Transactions of the International Society for Music Information Retrieval, 2020 Pitch-class distributions are of central relevance in music information retrieval, computational musicology and various other fields, such as music perception and cognition. However, despite their structure being closely related to the cognitively and musically relevant properties of a piece, many existing approaches treat pitch-class distributions as ...
Robert Lieck +2 more
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A Comprehensive Survey on Diffusion Model-Driven 3D Reconstruction: Methods, Datasets, and Prospects
ISPRS International Journal of Geo-InformationThree-dimensional (3D) reconstruction serves as a key technology bridging the real and digital worlds, with broad application in remote sensing, autonomous driving, and robotics.
Qianwen Yao +5 more
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Physical Review E, 2008 We study here the random diffusion model. This is a continuum model for a conserved scalar density field $ϕ$ driven by diffusive dynamics. The interesting feature of the dynamics is that the {\it bare} diffusion coefficient $D$ is density dependent. In the simplest case $D=\bar{D}+D_{1}δϕ$ where $\bar{D}$ is the constant average diffusion constant.
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MathematicsIn this study, we introduce a novel model, the Combined Model, composed of a conditional denoising diffusion model (SR3) and an enhanced residual network (EResNet), for reconstructing high-resolution turbulent flow fields from low-resolution flow data ...
Jiaheng Qi, Hongbing Ma
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