9 citations · 11 across the 7 of their papers we have counts for
7 papers
Midpoint Generative Models
Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2
We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow Matching with linear interp…
Unlocking the Duality between Flow and Field Matching
Daniil Shlenskii, Alexander Varlamov, Nazar Buzun +1
Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion models and flow matching. Interaction Field Matching (IFM) is a newer framework that gen…
Overclocking Electrostatic Generative Models
Daniil Shlenskii, Alexander Korotin
Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving competitive performance in image synthesis. PFGM++ operates in an extended d…
HOTA: Hamiltonian framework for Optimal Transport Advection
Nazar Buzun, Daniil Shlenskii, Maxim Bobrin +1
Optimal transport (OT) has become a natural framework for guiding the probability flows. Yet, the majority of recent generative models assume trivial geometry (e.g., Euclidean) and…
Does Diffusion Beat GAN in Image Super Resolution?
Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii +1
There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based IS…
YaART: Yet Another ART Rendering Technology
Sergey Kastryulin, Artem Konev, Alexander Shishenya +20
In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study…
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev +3
Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the rec…