Fetching the paper…
Reading the bibliography…
Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based models.
A family of embedded runge-kutta formulae
J. R. Dormand and P. J. Prince · 1980
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, K. Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Optimal transport: old and new
Cédric Villani et al · 2009
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
Earlier work this paper cites.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Earlier work this paper cites.
High-resolution image synthesis and semantic manipulation with conditional gans
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
Earlier work this paper cites.
Edgeconnect: Generative image inpainting with adversarial edge learning
Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Z Qureshi, and Mehran Ebrahimi · 2019
Earlier work this paper cites.
Semantic image synthesis with spatially-adaptive normalization
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu · 2019
Earlier work this paper cites.
Computational Optimal Transport: With Applications to Data Science
Gabriel Peyré and Marco Cuturi · 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.
Free-form image inpainting with gated convolution
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Earlier work this paper cites.
On implicit regularization in β \beta -vaes
Abhishek Kumar and Ben Poole · 2020
Earlier work this paper cites.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Dual attention gans for semantic image synthesis
Hao Tang, Song Bai, and Nicu Sebe · 2020
Cited alongside, same era.
Towards a theoretical understanding of the robustness of variational autoencoders
Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, and Tom Rainforth · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
ImageBART: Bidirectional context with multinomial diffusion for autoregressive image synthesis
Patrick Esser, Robin Rombach, Andreas Blattmann, and Björn Ommer · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Later among the works it cites.
Autoregressive image generation using residual quantization
Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han · 2022
Later among the works it cites.
Mat: Mask-aware transformer for large hole image inpainting
Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, and Jiaya Jia · 2022
Later among the works it cites.
Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
Later among the works it cites.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 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…
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Cited alongside, same era.
A variational perspective on diffusion-based generative models and score matching
Chin-Wei Huang, Jae Hyun Lim, and Aaron C Courville · 2021
Cited alongside, same era.
Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
Cited alongside, same era.
William Peebles and Saining Xie · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
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.
Stylegan-xl: Scaling stylegan to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
Later among the works it cites.
Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2022
Later among the works it cites.
Resolution-robust large mask inpainting with fourier convolutions
Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky · 2022
Later among the works it cites.
Semantic image synthesis via diffusion models
Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, and Houqiang Li · 2022
Later among the works it cites.
Fast point cloud generation with straight flows
Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, and Qiang Liu · 2022
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
Later among the works it cites.
Entropy-driven sampling and training scheme for conditional diffusion generation
Guangcong Zheng, Shengming Li, Hui Wang, Taiping Yao, Yang Chen, Shouhong Ding, and Xi Li · 2022
Later among the works it cites.
Riemannian flow matching on general geometries
Ricky TQ Chen and Yaron Lipman · 2023
Closest in time.
Diffusion language models, 2023
Sander Dieleman · 2023
Closest in time.
Minimizing trajectory curvature of ode-based generative models
Sangyun Lee, Beomsu Kim, and Jong Chul Ye · 2023
Closest in time.
Self-consistent velocity matching of probability flows
Lingxiao Li, Samuel Hurault, and Justin Solomon · 2023
Closest in time.
Flow matching for generative modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
Closest in time.
Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
Closest in time.
Wavelet diffusion models are fast and scalable image generators
Hao Phung, Quan Dao, and Anh Tran · 2023
Closest in time.
Multisample flow matching: Straightening flows with minibatch couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky Chen · 2023
Closest in time.
Consistency models, 2023
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Closest in time.
Diffusion-GAN: Training GANs with diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, and Mingyuan Zhou · 2023
Closest in time.
Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2023
Closest in time.
Improved techniques for maximum likelihood estimation for diffusion odes
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2023
Closest in time.