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Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation.
Scoring rules for continuous probability distributions
James E Matheson and Robert L Winkler · 1976
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
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A brief introduction to boosting
Robert E Schapire · 1999
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A field guide to dynamical recurrent networks
John F Kolen and Stefan C Kremer · 2001
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Recognizing human actions: a local svm approach
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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U-net: Convolutional networks for biomedical image segmentation
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Delving deeper into convolutional networks for learning video representations
Nicolas Ballas, Li Yao, Chris Pal, and Aaron C Courville · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
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Video frame synthesis using deep voxel flow
Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala · 2017
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Deep predictive coding networks for video prediction and unsupervised learning
William Lotter, Gabriel Kreiman, and David Cox · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Sandra Aigner and Marco Körner · 2018
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine · 2018
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Long-term on-board prediction of people in traffic scenes under uncertainty
Apratim Bhattacharyya, Mario Fritz, and Bernt Schiele · 2018
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Contextvp: Fully context-aware video prediction
Wonmin Byeon, Qin Wang, Rupesh Kumar Srivastava, and Petros Koumoutsakos · 2018
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Towards lattice Boltzmann models for climate sciences: The GeLB programming language with applications
Dragos Bogdan Chirila · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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David Ha and Jürgen Schmidhuber · 2018
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A review on deep learning techniques for video prediction
Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros · 2020
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A short note on the kinetics-700-2020 human action dataset
Lucas Smaira, João Carreira, Eric Noland, Ellen Clancy, Amy Wu, and Andrew Zisserman · 2020
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Learning for video compression with recurrent auto-encoder and recurrent probability model
Ren Yang, Fabian Mentzer, Luc Van Gool, and Radu Timofte · 2020
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Learning to forecast and refine residual motion for image-to-video generation
Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, and Dimitris Metaxas · 2020
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Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Disentangled sequential autoencoder
Yingzhen Li and Stephan Mandt · 2018
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Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Improved conditional vrnns for video prediction
Lluis Castrejon, Nicolas Ballas, and Aaron Courville · 2019
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Adversarial video generation on complex datasets
Aidan Clark, Jeff Donahue, and Karen Simonyan · 2019
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
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Diffwave: A versatile diffusion model for audio synthesis
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Diffusion probabilistic models for 3d point cloud generation
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Predictive coding, variational autoencoders, and biological connections
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Improving sequential latent variable models with autoregressive flows
Joseph Marino, Lei Chen, Jiawei He, and Stephan Mandt · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
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Skilful precipitation nowcasting using deep generative models of radar
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Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Greedy hierarchical variational autoencoders for large-scale video prediction
Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, and Chelsea Finn · 2021
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Progressive distillation for fast sampling of diffusion models
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An introduction to neural data compression
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