3 citations · 4 across the 8 of their papers we have counts for
9 papers
Simultaneous Neural Optimal Transport
Milena Gazdieva, Kirill Sokolov, Jiawei Chen +2
Optimal Transport (OT) provides a principled framework for learning transformations between probability distributions from unpaired samples. In many applications, however, a single…
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva +2
Neural network-based optimal transport (OT) is a recent and fruitful direction in the generative modeling community. It finds its applications in various fields such as domain tran…
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Milena Gazdieva, Jaemoo Choi, Alexander Kolesov +3
Aggregating data from multiple sources can be formalized as an Optimal Transport (OT) barycenter problem, which seeks to compute the average of probability distributions with respe…
Estimating Barycenters of Distributions with Neural Optimal Transport
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +4
Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions. A theoretically ap…
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +5
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter tas…
Light Unbalanced Optimal Transport
Milena Gazdieva, Arip Asadulaev, Alexander Korotin +1
While the continuous Entropic Optimal Transport (EOT) field has been actively developing in recent years, it became evident that the classic EOT problem is prone to different issue…
Extremal Domain Translation with Neural Optimal Transport
Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych +1
In many unpaired image domain translation problems, e.g., style transfer or super-resolution, it is important to keep the translated image similar to its respective input image. We…
An Optimal Transport Perspective on Unpaired Image Super-Resolution
Milena Gazdieva, Petr Mokrov, Litu Rout +4
Real-world image super-resolution (SR) tasks often do not have paired datasets, which limits the application of supervised techniques. As a result, the tasks are usually approached…
Can We Use Neural Regularization to Solve Depth Super-Resolution?
Milena Gazdieva, Oleg Voynov, Alexey Artemov +3
Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational probl…