Properties of Discrete Sliced Wasserstein Losses

Abstract

The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to optimise some parameters in order to minimise SW, which serves as a loss function between discrete probability measures (since measures admitting densities are numerically unattainable). All these optimisation problems bear the same sub-problem, which is minimising the Sliced Wasserstein energy. In this paper we study the properties of E: Y SW_2^2(_Y, _Z), i.e. the SW distance between two uniform discrete measures with the same amount of points as a function of the support Y R^n d of one of the measures. We investigate the regularity and optimisation properties of this energy, as well as its Monte-Carlo approximation E_p (estimating the expectation in SW using only p samples) and show convergence results on the critical points of E_p to those of E, as well as an almost-sure uniform convergence and a uniform Central Limit result on the process E_p(Y). Finally, we show that in a certain sense, Stochastic Gradient Descent methods minimising E and E_p converge towards (Clarke) critical points of these energies.

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Reproductions