Fetching the paper…
Reading the bibliography…
Diffusion models are widely used in image generation because they can generate high-quality and realistic samples.
Nowcasting challenges during the beijing olympics: Successes, failures, and implications for future nowcasting systems
James W Wilson, Yerong Feng, Min Chen, and Rita D Roberts · 2010
Earlier work this paper cites.
Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, and et al · 2015
Earlier work this paper cites.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, and Wai-Kin Wong · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Deep learning for precipitation nowcasting: A benchmark and a new model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo · 2017
Earlier work this paper cites.
Dual motion gan for future-flow embedded video prediction
Xiaodan Liang, Lisa Lee, Wei Dai, and Eric P Xing · 2017
Earlier work this paper cites.
Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
Earlier work this paper cites.
Making the black box more transparent: Understanding the physical implications of machine learning
Amy McGovern, Ryan Lagerquist, David John Gagne, G Eli Jergensen, Kimberly L Elmore, Cameron R Homeyer, and Travis Smith · 2019
Earlier work this paper cites.
Improving high-impact numerical weather prediction with lidar and drone observations
Daniel Leuenberger, Alexander Haefele, Nadja Omanovic, Martin Fengler, Giovanni Martucci, Bertrand Calpini, Oliver Fuhrer, and Andrea Rossa · 2020
Earlier work this paper cites.
U2-net: Going deeper with nested u-structure for salient object detection
Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R Zaiane, and Martin Jagersand · 2020
Earlier work this paper cites.
Imaginator: Conditional spatio-temporal gan for video generation
Yaohui Wang, Piotr Bilinski, Francois Bremond, and Antitza Dantcheva · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Smaat-unet: Precipitation nowcasting using a small attention-unet architecture
Katharina Trebing, Tomasz Stańczyk, and Siamak Mehrkanoon · 2021
Cited alongside, same era.
Skilful precipitation nowcasting using deep generative models of radar
S. Ravuri, M. Willson, and et al · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Skilful precipitation nowcasting using deep generative models of radar
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, et al · 2021
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
A spatiotemporal deep learning model st-lstm-sa for hourly rainfall forecasting using radar echo images
Jie Liu, Liang Xu, and Ning Chen · 2022
Later among the works it cites.
The reconstitution predictive network for precipitation nowcasting
Chuyao Luo, Guangning Xu, Xutao Li, and Yunming Ye · 2022
Later among the works it cites.
Two-stage ua-gan for precipitation nowcasting
Liujia Xu, Dan Niu, Tianbao Zhang, Pengju Chen, Xunlai Chen, and Yinghao Li · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Rotate to attend: Convolutional triplet attention module
Diganta Misra, Trikay Nalamada, Ajay Uppili Arasanipalai, and Qibin Hou · 2021
Cited alongside, same era.
Prediction of flow based on a cnn-lstm combined deep learning approach
Peng Li, Jie Zhang, and Peter Krebs · 2022
Cited alongside, same era.
Recurrent flow networks: A recurrent latent variable model for density estimation of urban mobility
Daniele Gammelli and Filipe Rodrigues · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Ilan Price and Stephan Rasp · 2022
Later among the works it cites.
A generative deep learning approach to stochastic downscaling of precipitation forecasts
Lucy Harris, Andrew TT McRae, Matthew Chantry, Peter D Dueben, and Tim N Palmer · 2022
Later among the works it cites.
Video diffusion models, 2022
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet · 2022
Later among the works it cites.
Robust recurrent neural networks for time series forecasting
Xueli Zhang, Cankun Zhong, Jianjun Zhang, Ting Wang, and Wing WY Ng · 2023
Later among the works it cites.
Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah · 2023
Later among the works it cites.
Medsegdiff: Medical image segmentation with diffusion probabilistic model
Junde Wu, RAO FU, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, and Yanwu Xu · 2023
Later among the works it cites.