no code implementations • 2 Feb 2022 • Milena Gazdieva, Litu Rout, Alexander Korotin, Andrey Kravchenko, Alexander Filippov, Evgeny Burnaev
First, the learned SR map is always an optimal transport (OT) map.
2 code implementations • NeurIPS 2021 • Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
6 code implementations • NeurIPS 2021 • Alexander Korotin, Lingxiao Li, Aude Genevay, Justin Solomon, Alexander Filippov, Evgeny Burnaev
Despite the recent popularity of neural network-based solvers for optimal transport (OT), there is no standard quantitative way to evaluate their performance.
1 code implementation • 25 May 2021 • Aleksandr Safin, Maxim Kan, Nikita Drobyshev, Oleg Voynov, Alexey Artemov, Alexander Filippov, Denis Zorin, Evgeny Burnaev
We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps.
1 code implementation • NeurIPS 2021 • Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
1 code implementation • 15 Jun 2020 • Ilya Trofimov, Nikita Klyuchnikov, Mikhail Salnikov, Alexander Filippov, Evgeny Burnaev
The method relies on a new approach to low-fidelity evaluations of neural architectures by training for a few epochs using a knowledge distillation.