AI Technology News

Your Daily Dose of AI Innovation & Insights

From Diffusion Modeling to Flow Matching for Generative AI – Abridged | by Technoids Blog | Jan, 2025

From Diffusion Modeling to Flow Matching for Generative AI – Abridged | by Technoids Blog | Jan, 2025

Put Ads For Free On FiverrClerks.com

References

S. H. Chan, Tutorial on Diffusion Models for Imaging and Vision, Sept. 2024
https://arxiv.org/abs/2403.18103

P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, D. Podell, T. Dockhorn, Z. English, K. Lacey, A. Goodwin, Y. Marek, R. Rombach, Scaling Rectified Flow Transformers for High-Resolution Image Synthesis, Proc. ICML, 2024
https://export.arxiv.org/pdf/2403.03206

D. Foster, Generative Deep Learning, 2nd ed., O’Reilly, Sebastopol, USA, 2023

J. Ho, A. Jain, and P. Abbeel, Denoising diffusion probabilistic models, Advances in Neural Information Processing Systems (NeurIPS), 2020
https://arxiv.org/abs/2006.11239

P. Holderrieth, M. Havasi, J. Yim, N. Shaul, I. Gat, T. Jaakkola, B. Karrer, R.T.Q. Chen, Y. Lipman, Generator Matching: Generative Modeling with Arbitrary Markov Processes, Computing Research Repository, 2024
https://arxiv.org/pdf/2410.20587

A. Hyvärinen, Estimation of non-normalized statistical models by score matching, Journal of Machine Learning Research (JMLR), Vol. 6, Issue 24, pp. 695–709, 2005
https://jmlr.org/papers/volume6/hyvarinen05a/hyvarinen05a.pdf

Y. Lipman, R.T.Q. Chen, H. Ben-Hamu, M. Nickel and M. Lee, Flow Matching for Generative Modeling, Proc. ICLR, 2023
https://arxiv.org/pdf/2210.02747

Y. Lipman, M. Havasi, P. Holderrieth, N. Shaul, M. Le, B. Karrer, R.T.Q. Chen, D. Lopez-Paz, H. Ben-Hamu, I. Gat, Flow Matching Guide and Code, Dec. 2024
https://arxiv.org/pdf/2412.06264

R. Po, W. Yifan, V. Golyanik, K. Aberman, J.T. Barron, A. Bermano, E. Chan, T. Dekel, A. Holynski, A. Kanazawa, C.K. Liu, L. Liu, B. Mildenhall, M. Nießner, B. Ommer, C. Theobalt, P. Wonka, G. Wetzstein, State of the Art on Diffusion Models for Visual Computing, Oct. 2023
https://arxiv.org/pdf/2310.07204

J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, Deep Unsupervised Learning using Nonequilibrium Thermodynamics, Proc. of Int. Conf. on Machine Learning (ICML), Vol. 27, pp. 2256–2265, 2015
https://arxiv.org/pdf/1503.03585

Y. Song and S. Ermon, Generative modeling by estimating gradients of the data distribution, In: Advances in Neural Information Processing Systems, pp. 11895–11907, 2019

J. Song, C. Meng, and S. Ermon, Denoising diffusion implicit models, Int. Conf. on Learning Representations (ICLR), 2021
https://openreview.net/forum?id=St1giarCHLP

N. Whiteley, A. Gray and P. Rubin-Delanchy, Statistical exploration of the Manifold Hypothesis, 2022
https://arxiv.org/pdf/2208.11665

D. Foster, Generative Deep Learning, 2nd ed., O’Reilly, Sebastopol, USA, 2023

Y. Song and S. Ermon, Generative modeling by estimating gradients of the data distribution, In: Advances in Neural Information Processing Systems, pp. 11895–11907, 2019

Put Ads For Free On FiverrClerks.com

About The Author

Leave a Reply

Your email address will not be published. Required fields are marked *

Copyright © All rights reserved. | Website by EzeSavers.
error: Content is protected !!