Awesome
From Denoising Diffusions to Denoising Markov Models
Running experiments
For the g-and-k distribution example, run
python main_score_sde.py experiment=conditional dataset.num_quantiles=250 dataset.num_samples=250
For the MNIST inpainting example, go into the discrete_ctmc
directory and run
python dist_train.py conditional_mnist
For the ImageNet super-resolution example, go into the discrete_ctmc
directory and run
python train.py conditional_imagenet
For the SO3 example, run
python main.py experiment=so3 dataset.K=16
For the pose estimation example, run
python main.py experiment=symsol
References
This codebase is largely based on the existing works of [1] and [2], both also developed at Oxford in the Department of Statistics. It also uses a modified version of geomstats and haikumodels.
[1] Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet, Riemannian Score-Based Generative Modeling, NeurIPS 2022.
[2] Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet, A Continuous Time Framework for Discrete Denoising Models