Fetching the paper…
Reading the bibliography…
Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 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.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Saleh, B. and Elgammal, A · 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.
Plug-and-play admm for image restoration: Fixed-point convergence and applications
Chan, S. H., Wang, X., and Elgendy, O. A · 2016
Earlier work this paper cites.
Arcface: Additive angular margin loss for deep face recognition
Deng, J., Guo, J., Xue, N., and Zafeiriou, S · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Earlier work this paper cites.
Ilvr: Conditioning method for denoising diffusion probabilistic models. in 2021 ieee
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
Cited alongside, same era.
Score-based generative neural networks for large-scale optimal transport
Daniels, M., Maunu, T., and Hand, P · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2022
Later among the works it cites.
Tevet, G., Raab, S., Gordon, B., Shafir, Y., Cohen-Or, D., and Bermano, A. H · 2022
Later among the works it cites.
Zero-shot image restoration using denoising diffusion null-space model
Wang, Y., Yu, J., and Zhang, J · 2022
Later among the works it cites.
Motiondiffuse: Text-driven human motion generation with diffusion model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., and Liu, Z · 2022
Later among the works it cites.
Universal guidance for diffusion models
Bansal, A., Chu, H.-M., Schwarzschild, A., Sengupta, S., Goldblum, M., Geiping, J., and Goldstein, T · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S · 2021
Cited alongside, same era.
Improving diffusion models for inverse problems using manifold constraints
Chung, H., Sim, B., Ryu, D., and Ye, J. C · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
Cited alongside, same era.
Denoising diffusion restoration models
Kawar, B., Elad, M., Ermon, S., and Song, J · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Diffusion posterior sampling for general noisy inverse problems
Chung, H., Kim, J., Mccann, M. T., Klasky, M. L., and Ye, J. C · 2023
Later among the works it cites.
Mou, C., Wang, X., Xie, L., Zhang, J., Qi, Z., Shan, Y., and Qie, X · 2023
Later among the works it cites.
Loss-guided diffusion models for plug-and-play controllable generation
Song, J., Zhang, Q., Yin, H., Mardani, M., Liu, M.-Y., Kautz, J., Chen, Y., and Vahdat, A · 2023
Later among the works it cites.
Freedom: Training-free energy-guided conditional diffusion model
Yu, J., Wang, Y., Zhao, C., Ghanem, B., and Zhang, J · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Zhang, L., Rao, A., and Agrawala, M · 2023
Later among the works it cites.
Denoising diffusion models for plug-and-play image restoration
Zhu, Y., Zhang, K., Liang, J., Cao, J., Wen, B., Timofte, R., and Van Gool, L · 2023
Later among the works it cites.
Manifold preserving guided diffusion
He, Y., Murata, N., Lai, C.-H., Takida, Y., Uesaka, T., Kim, D., Liao, W.-H., Mitsufuji, Y., Kolter, J. Z., Salakhutdinov, R., et al · 2024
Closest in time.