Results 21 to 30 of about 572,440 (170)

Autoregressive Diffusion Models

CoRR, 2021
We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train. Unlike standard ARMs, they do
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans   +5 more
openaire   +3 more sources

DENOISING DIFFUSION PROBABILISTIC MODEL WITH WAVELET PACKET TRANSFORM FOR FINGERPRINT GENERATION

Jordanian Journal of Computers and Information Technology
The majority of contemporary fingerprint synthesis is based on the Generative Adversarial Network (GAN). Recently, the Denoising Diffusion Probabilistic Model (DDPM) has been demonstrated to be more effective than GAN in numerous scenarios, particularly ...
LI CHEN, YONG HUAH CHAN
doaj   +1 more source

Deformation and failure characteristics of gas drainage drilling-reaming coal mass in non-uniform stress field

Meitan xuebao
Clarifying the internal relationship between pore structure and methane adsorption and diffusion in coal is the basis for quantitative evaluation of methane desorption.
Yuanping CHENG, Minghao YI, Liang WANG
doaj   +1 more source

Diffusion-Model-Based Downscaling of Observed Sea Surface Height over the Kuroshio Extension Since 2000

Atmosphere
Satellite altimetry measurements enable the resolution of ocean variability from basin-scale to mesoscale. However, the spatial resolution is still limited.
Qiuchang Han, Xingliang Jiang, Yang Zhao, Xudong Wang   +3 more
doaj   +1 more source

Diffusion Recommender Model

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. However, these generative models suffer from intrinsic limitations such as the instability of GANs and the restricted representation ability of VAEs.
Wenjie Wang 0007, Yiyan Xu, Fuli Feng, Xinyu Lin 0001, Xiangnan He 0001, Tat-Seng Chua   +5 more
openaire   +2 more sources

Advancing Traditional Dunhuang Regional Pattern Design with Diffusion Adapter Networks and Cross-Entropy

Entropy
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large ...
Yihuan Tian, Tao Yu, Zuling Cheng, Sunjung Lee   +3 more
doaj   +1 more source

DiffBTS: A Lightweight Diffusion Model for 3D Multimodal Brain Tumor Segmentation

Sensors
Denoising diffusion probabilistic models (DDPMs) have achieved remarkable success across various research domains. However, their high complexity when processing 3D images remains a limitation. To mitigate this, researchers typically preprocess data into
Zuxin Nie, Jiahong Yang, Chengxuan Li, Yaqin Wang, Jun Tang   +4 more
doaj   +1 more source

MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments

Plants, 2023
The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally.
Haoyu Wang, Jie Ding, Sifan He, Cheng Feng, Cheng Zhang, Guohua Fan, Yunzhi Wu, Youhua Zhang   +7 more
doaj   +1 more source

A state-of-the-art review of diffusion model applications for microscopic image and micro-alike image analysis

Frontiers in Medicine
Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process.
Yan Liu, Tao Jiang, Rui Li, Lingling Yuan, Marcin Grzegorzek, Chen Li, Xiaoyan Li   +6 more
doaj   +1 more source

Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model

Remote Sensing, 2023
Recently, optical remote-sensing images have been widely applied in fields such as environmental monitoring and land cover classification. However, due to limitations in imaging equipment and other factors, low-resolution images that are unfavorable for ...
Lintao Han, Yuchen Zhao, Hengyi Lv, Yisa Zhang, Hailong Liu, Guoling Bi, Qing Han   +6 more
doaj   +1 more source