Fetching the paper…
Reading the bibliography…
Recent advances in diffusion models bring state-of-the-art performance on image generation tasks.
Reverse-time diffusion equation models
Anderson, B. D · 1982
Earlier work this paper cites.
On free energy, stochastic control, and schrödinger processes
Pavon, M. and Wakolbinger, A · 1991
Earlier work this paper cites.
Partial differential equations
Evans, L. C · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
Earlier work this paper cites.
Stochastic differential equations: an introduction with applications
Oksendal, B · 2013
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Van Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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
Earlier work this paper cites.
Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Earlier work this paper cites.
Image transformer
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., and Tran, D · 2018
Cited alongside, same era.
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Fano’s inequality for random variables
Gerchinovitz, S., Ménard, P., and Stoltz, G · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Variational diffusion models
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
Closest in time.
Sdedit: Guided image synthesis and editing with stochastic differential equations
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
Closest in time.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Closest in time.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
Closest in time.
Solving schrödinger bridges via maximum likelihood
Vargas, F., Thodoroff, P., Lamacraft, A., and Lawrence, N · 2021
Closest in time.
Likelihood training of schrödinger bridge using forward-backward SDEs theory
Chen, T., Liu, G.-H., and Theodorou, E · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Cited alongside, same era.
Nvae: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Densely connected normalizing flows
Grcić, M., Grubišić, I., and Šegvić, S · 2021
Cited alongside, same era.
Autoregressive diffusion models
Hoogeboom, E., Gritsenko, A. A., Bastings, J., Poole, B., Berg, R. v. d., and Salimans, T · 2021
Cited alongside, same era.
Score-based generative modeling with critically-damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K · 2022
Closest in time.
Hazami, L., Mama, R., and Thurairatnam, R · 2022
Closest in time.
Maximum likelihood training of implicit nonlinear diffusion models
Kim, D., Na, B., Kwon, S. J., Lee, D., Kang, W., and Moon, I.-C · 2022
Closest in time.
Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z · 2022
Closest in time.
Styleformer: Transformer based generative adversarial networks with style vector
Park, J. and Kim, Y · 2022
Closest in time.
On buggy resizing libraries and surprising subtleties in fid calculation
Parmar, G., Zhang, R., and Zhu, J.-Y · 2022
Closest in time.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2022
Closest in time.