Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
We present an efficient algorithm for regularized optimal transport. In contrast to previous methods, we use the Douglas-Rachford splitting technique to develop an efficient solver that can handle a broad class of regularizers. The algorithm has strong global convergence guarantees, low per-iteration cost, and can exploit GPU parallelization, making it considerably faster than the state-of-the-art for many problems. We illustrate its competitiveness in several applications, including domain adaptation and learning of generative models.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationGPUSimilar Papers 제목 키워드 기반
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, the…
RetrievalSinkhorn Distances: Lightspeed Computation of Optimal Transport
Optimal transportation distances are a fundamental family of parameterized distances for histograms in the probability simplex. Despite their appealing theoretical properties, excellent performance and intuitive formulat…
A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation
We develop a fast and reliable method for solving large-scale optimal transport (OT) problems at an unprecedented combination of speed and accuracy. Built on the celebrated Douglas-Rachford splitting technique, our metho…
GPURegularized Discrete Optimal Transport
This article introduces a generalization of the discrete optimal transport, with applications to color image manipulations. This new formulation includes a relaxation of the mass conservation constraint and a regularizat…
ColorizationColor NormalizationExploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering
We propose a methodology to explore and measure the pairwise correlations that exist between variables in a dataset. The methodology leverages copulas for encoding dependence between two variables, state-of-the-art optim…
Clustering