ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced
  Wasserstein Distance for Variance Reduction
Distribution matching is central to many vision and graphics tasks, where the
widely used Wasserstein distance is too costly to compute for high dimensional
distributions. The Sliced Wasserstein Distance (SWD) offers a scalable
alternative, yet its Monte Carlo estimator suffers from high variance,
resulting in noisy gradients and slow convergence. We introduce Reservoir SWD
(ReSWD), which integrates Weighted Reservoir Sampling into SWD to adaptively
retain informative projection directions in optimization steps, resulting in
stable gradients while remaining unbiased. Experiments on synthetic benchmarks
and real-world tasks such as color correction and diffusion guidance show that
ReSWD consistently outperforms standard SWD and other variance reduction
baselines. Project page: https://reservoirswd.github.io/
68de21df6024653e8a3ed1cf
Mark Boss
claimed_verified
2025-10-02T13:54:44.039Z
64b7f06fda8017900e893eb4
https://cdn-avatars.huggingface.co/v1/production/uploads/64b7f06fda8017900e893eb4/3VcQFjERXyAjHQFLMFEJt.jpeg
Mark Boss
user
mboss
68de21df6024653e8a3ed1d0
Andreas Engelhardt
68de21df6024653e8a3ed1d1
Simon Donné
68de21df6024653e8a3ed1d2
Varun Jampani
ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced
  Wasserstein Distance for Variance Reduction
Distribution matching is central to many vision and graphics tasks, where the
widely used Wasserstein distance is too costly to compute for high dimensional
distributions. The Sliced Wasserstein Distance (SWD) offers a scalable
alternative, yet its Monte Carlo estimator suffers from high variance,
resulting in noisy gradients and slow convergence. We introduce Reservoir SWD
(ReSWD), which integrates Weighted Reservoir Sampling into SWD to adaptively
retain informative projection directions in optimization steps, resulting in
stable gradients while remaining unbiased. Experiments on synthetic benchmarks
and real-world tasks such as color correction and diffusion guidance show that
ReSWD consistently outperforms standard SWD and other variance reduction
baselines. Project page: https://reservoirswd.github.io/
Reservoir SWD reduces variance in Sliced Wasserstein Distance, improving gradient stability and performance in vision and graphics tasks.
Sliced Wasserstein Distance SWD Weighted Reservoir Sampling Reservoir SWD ReSWD variance reduction gradient stability color correction diffusion guidance