Papers › Sliced Wasserstein Generative Models

Sliced Wasserstein Generative Models

10 Apr 2019CVPR 2019 6arXiv:1904.05408archive 2025-07-28

Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions. In contrast, the sliced Wasserstein distance (SWD) factorizes high-dimensional distributions into their multiple one-dimensional marginal distributions and is thus easier to approximate. In this paper, we introduce novel approximations of the primal and dual SWD. Instead of using a large number of random projections, as it is done by conventional SWD approximation methods, we propose to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion. As concrete applications of our SWD approximations, we design two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders (AE) and Generative Adversarial Networks (GAN). In the experiments, we not only show the superiority of the proposed generative models on standard image synthesis benchmarks, but also demonstrate the state-of-the-art performance on challenging high resolution image and video generation in an unsupervised manner.

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musikisomorphie/swd officialmentioned in papermentioned on GitHubtf report

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Tasks

Image GenerationVideo Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation CelebA-HQ 1024x1024 PG-SWGAN FID 5.5 #3 of 10 Archive leaderboard report
Image Generation LSUN Bedroom 256 x 256 PG-SWGAN FID 8.0 #15 of 32 Archive leaderboard report
Video Generation TrailerFaces PG-SWGAN-3D FID 404.1 #1 of 1 Archive leaderboard report

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