18 citations · 98 across the 27 of their papers we have counts for
5 papers · 1 filter
Generative Modeling with Optimal Transport Maps
Litu Rout, Alexander Korotin, Evgeny Burnaev
With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as th…
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov +4
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, giv…
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Alexander Korotin, Lingxiao Li, Aude Genevay +3
Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance. In this paper, we add…
Large-Scale Wasserstein Gradient Flows
Petr Mokrov, Alexander Korotin, Lingxiao Li +3
Wasserstein gradient flows provide a powerful means of understanding and solving many diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of proba…
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Alexander Korotin, Lingxiao Li, Justin Solomon +1
Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to com…