Fetching the paper…
Reading the bibliography…
Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult.
Recommendation ITU-T T.81: Information technology – Digital compression and coding of continuous-tone still images – Requirements and guidelines, 1992
ITU-T · 1992
Earlier work this paper cites.
PhotoCD PCD0992, 1993
Kodak · 1993
Earlier work this paper cites.
Generative Adversarial Nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2017
Earlier work this paper cites.
Real-time adaptive image compression
O. Rippel and L. Bourdev · 2017
Earlier work this paper cites.
The perception-distortion tradeoff
Y. Blau and T. Michaeli · 2018
Earlier work this paper cites.
Generative compression
Shibani Santurkar, David Budden, and Nir Shavit · 2018
Earlier work this paper cites.
Generative adversarial networks for extreme learned image compression
Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, and Luc Van Gool · 2019
Earlier work this paper cites.
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
M. Havasi, R. Peharz, and J. M. Hernández-Lobato · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
High-fidelity generative image compression
Fabian Mentzer, George D Toderici, Michael Tschannen, and Eirikur Agustsson · 2020
Earlier work this paper cites.
Channel-wise autoregressive entropy models for learned image compression
David Minnen and Saurabh Singh · 2020
Earlier work this paper cites.
A gan-based tunable image compression system
Lirong Wu, Kejie Huang, and Haibin Shen · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
High-fidelity generative image compression
Fabian Mentzer, George D Toderici, Michael Tschannen, and Eirikur Agustsson · 2020
Earlier work this paper cites.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Earlier work this paper cites.
On density estimation with diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Saliency driven perceptual image compression
Yash Patel, Srikar Appalaraju, and R. Manmatha · 2021
Cited alongside, same era.
Palette: Image-to-Image Diffusion Models
Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi · 2021
Cited alongside, same era.
Image super-resolution via iterative refinement, 2021
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Later among the works it cites.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 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…
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
On the advantages of stochastic encoders
L. Theis and E. Agustsson · 2021
Cited alongside, same era.
Universal rate-distortion-perception representations for lossy compression
George Zhang, Jingjing Qian, Jun Chen, and Ashish J Khisti · 2021
Cited alongside, same era.
On density estimation with diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Challenge on Learned Image Compression, 2022
2022
Cited alongside, same era.
L. Theis, T. Salimans, M. D. Hoffman, and F. Mentzer · 2022
Later among the works it cites.
Algorithms for the communication of samples
L. Theis and N. Yosri · 2022
Later among the works it cites.
Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
Later among the works it cites.
Multi-realism image compression with a conditional generator, 2022
Eirikur Agustsson, David Minnen, George Toderici, and Fabian Mentzer · 2022
Later among the works it cites.
Image compression with product quantized masked image modeling
Alaaeldin El-Nouby, Matthew J Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, and Hervé Jégou · 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.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo-Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
On the importance of noise scheduling for diffusion models
Ting Chen · 2023
Closest in time.
Neural image compression with a diffusion-based decoder, 2023
Noor Fathima Ghouse, Jens Petersen, Auke Wiggers, Tianlin Xu, and Guillaume Sautière · 2023
Closest in time.
Neural image compression with a diffusion-based decoder
Noor Fathima Goose, Jens Petersen, Auke Wiggers, Tianlin Xu, and Guillaume Sautiere · 2023
Closest in time.
Simple diffusion: End-to-end diffusion for high resolution images, 2023
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans · 2023
Closest in time.
Toward semantic communications: Deep learning-based image semantic coding
Danlan Huang, Feifei Gao, Xiaoming Tao, Qiyuan Du, and Jianhua Lu · 2023
Closest in time.
Lossy image compression with conditional diffusion models, 2023
Ruihan Yang and Stephan Mandt · 2023
Closest in time.
Neural image compression with a diffusion-based decoder
Noor Fathima Goose, Jens Petersen, Auke Wiggers, Tianlin Xu, and Guillaume Sautiere · 2023
Closest in time.
Lossy image compression with conditional diffusion models, 2023
Ruihan Yang and Stephan Mandt · 2023
Closest in time.