Fetching the paper…
Reading the bibliography…
We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM).
Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Jarzynski, C · 1997
Earlier work this paper cites.
Computer methods for ordinary differential equations and differential-algebraic equations
Ascher, U. M. and Petzold, L. R · 1998
Earlier work this paper cites.
Introduction to electrodynamics, 2005
Griffiths, D. J · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Resiliency of deep neural networks under quantization
Sung, W., Shin, S., and Hwang, K · 2015
Earlier work this paper cites.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Post training 4-bit quantization of convolutional networks for rapid-deployment
Banner, R., Nahshan, Y., and Soudry, D · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
Cited alongside, same era.
Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
Cited alongside, same era.
Perception prioritized training of diffusion models
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., and Yoon, S · 2022
Later among the works it cites.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
Later among the works it cites.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2021
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M
Cited in the paper.
Point-e: A system for generating 3d point clouds from complex prompts
Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., and Chen, M
Cited in the paper.
Later among the works it cites.
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., Lauko, A., Bortoli, V. D., Mathieu, E., Barzilay, R., Jaakkola, T., DiMaio, F., Baek, M., and Baker, D · 2022
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
Later among the works it cites.
Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T · 2022
Later among the works it cites.
Lion: Latent point diffusion models for 3d shape generation
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., and Kreis, K · 2022
Later among the works it cites.
Stable target field for reduced variance score estimation in diffusion models
Xu, Y., Tong, S., and Jaakkola, T · 2023
Closest in time.