Fetching the paper…
Reading the bibliography…
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation.
Training generative adversarial networks with limited data, 2020a
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Taming transformers for high-resolution image synthesis, 2020
Patrick Esser, Robin Rombach, and Björn Ommer · 2012
Earlier work this paper cites.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes, 2014
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models, 2014
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, and Max Welling · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Importance weighted autoencoders, 2016
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2016
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks, 2016
Alexey Dosovitskiy and Thomas Brox · 2016
Earlier work this paper cites.
Variational inference with normalizing flows, 2016
Danilo Jimenez Rezende and Shakir Mohamed · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Ladder variational autoencoders, 2016
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, Christopher P. Burgess, Xavier Glorot, M. Botvinick, S. Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Improving variational inference with inverse autoregressive flow, 2017
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Earlier work this paper cites.
Understanding disentangling in β \beta -vae, 2018
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models, 2018
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2018
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation, 2018
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Earlier work this paper cites.
Spectral normalization for generative adversarial networks, 2018
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Neural discrete representation learning, 2018
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Resampled priors for variational autoencoders
M. Bauer and A. Mnih · 2019
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders, 2019
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2019
Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models, 2021
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis, 2021
Prafulla Dhariwal and Alex Nichol · 2021
Later among the works it cites.
Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2021
Later among the works it cites.
Variational diffusion models, 2021
Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Later among the works it cites.
Knowledge distillation in iterative generative models for improved sampling speed, 2021
Eric Luhman and Troy Luhman · 2021
Later among the works it cites.
Diffusion probabilistic models for 3d point cloud generation, 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bin Dai and David Wipf · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks, 2019
Tero Karras, Samuli Laine, and Timo Aila · 2019
Cited alongside, same era.
Learning non-convergent non-persistent short-run mcmc toward energy-based model, 2019
Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
Cited alongside, same era.
Quality aware generative adversarial networks
KANCHARLA PARIMALA and Sumohana Channappayya · 2019
Cited alongside, same era.
Generating diverse high-fidelity images with vq-vae-2, 2019
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
Cited alongside, same era.
Sylvester normalizing flows for variational inference, 2019
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, and Max Welling · 2019
Cited alongside, same era.
Ncp-vae: Variational autoencoders with noise contrastive priors
Jyoti Aneja, Alexander G. Schwing, Jan Kautz, and Arash Vahdat · 2020
Cited alongside, same era.
Shitong Luo and Wei Hu · 2021
Later among the works it cites.
The thermodynamic variational objective, 2021
Vaden Masrani, Tuan Anh Le, and Frank Wood · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models, 2021
Alex Nichol and Prafulla Dhariwal · 2021
Later among the works it cites.
Dual contradistinctive generative autoencoder
Gaurav Parmar, Dacheng Li, Kwonjoon Lee, and Zhuowen Tu · 2021
Later among the works it cites.
High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Later among the works it cites.
Image super-resolution via iterative refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
Later among the works it cites.
D2c: Diffusion-denoising models for few-shot conditional generation
Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Consistency regularization for variational auto-encoders, 2021
Samarth Sinha and Adji B. Dieng · 2021
Later among the works it cites.
Aligning latent and image spaces to connect the unconnectable
Ivan Skorokhodov, Grigorii Sotnikov, and Mohamed Elhoseiny · 2021
Later among the works it cites.
Nvae: A deep hierarchical variational autoencoder, 2021
Arash Vahdat and Jan Kautz · 2021
Later among the works it cites.
Score-based generative modeling in latent space, 2021
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Later among the works it cites.
Learning to efficiently sample from diffusion probabilistic models, 2021
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
Later among the works it cites.
Vaebm: A symbiosis between variational autoencoders and energy-based models, 2021
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2021
Later among the works it cites.
Diffusion autoencoders: Toward a meaningful and decodable representation
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn · 2022
Closest in time.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Closest in time.
Tackling the generative learning trilemma with denoising diffusion GANs
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
Closest in time.