Distributional Sliced-Wasserstein distance code

Distributional Sliced Wasserstein distance

This is a pytorch implementation of the paper "Distributional Sliced-Wasserstein and Applications to Generative Modeling". The work was done during the residency at VinAI Research, Hanoi, Vietnam.

Requirement

• python3.6
• pytorch 1.3
• torchvision
• numpy
• tqdm

Train on MNIST and FMNIST

python mnist.py \
    --datadir='./' \
    --outdir='./result' \
    --batch-size=512 \
    --seed=16 \
    --p=2 \
    --lr=0.0005 \
    --dataset='MNIST'
    --model-type='DSWD'\
    --latent-size=32 \ 

model-type in (SWD|MSWD|DSWD|GSWD|DGSWD|JSWD|JMSWD|JDSWD|JGSWD|JDGSWD|MGSWNN|JMGSWNN|MGSWD|JMGSWD)

Options for Sliced distances (number of projections used to approximate the distances)

--num-projection=1000

Options for Max Sliced-Wasserstein distance and Distributional distances (number of gradient steps for find the max slice or the optimal push-forward function):

--niter=10

Options for Distributional Sliced-Wasserstein Distance and Distributional Generalized Sliced-Wasserstein Distance (regularization strength)

--lam=10

Options for Generalized Wasserstein Distance (using circular function for Generalized Radon Transform)

--r=1000;\
--g='circular'

Train on CELEBA and CIFAR10 and LSUN

python main.py \
    --datadir='./' \
    --outdir='./result' \
    --batch-size=512 \
    --seed=16 \
    --p=2 \
    --lr=0.0005 \
    --model-type='DSWD'\
    --dataset='CELEBA'
    --latent-size=100 \ 

model-type in (SWD|MSWD|DSWD|GSWD|DGSWD|CRAMER)

Options for Sliced distances (number of projections used to approximate the distances)

--num-projection=1000

Options for Max Sliced-Wasserstein distance and Distributional distances (number of gradient steps for find the max slice or the optimal push-forward function):

--niter=1

Options for Distributional Sliced-Wasserstein Distance and Distributional Generalized Sliced-Wasserstein Distance (regularization strength)

--lam=1

Options for Generalized Wasserstein Distance (using circular function for Generalized Radon Transform)

--r=1000;\
--g='circular'

Some generated images

MNIST generated images

CELEBA generated images

LSUN generated images