Основные понятия
Establishing convergence results for OT-Flow in deep generative models.
Аннотация
The article discusses the convergence analysis of OT-Flow, a deep generative model. It focuses on establishing convergence results for OT-Flow, reformulating it to optimize the velocity field using neural networks. The content covers the theoretical framework, mathematical principles, and rigorous proofs related to the convergence of OT-Flow to optimal transport problems. It also delves into Monte Carlo approximation and the large data limit in training neural networks for sample generation.
Introduction to Deep Generative Models.
Frameworks like CNFs and DPMs.
Mathematical principles behind generative models.
Convergence analysis of OT-Flow.
Monte Carlo approximation and large data limit.
Статистика
"Z T 0 Z D |m|2 dxdt"
"R T 0 B2(ρt, mt)dt"
"Z T 0 Z D |m|2 ρ dxdt"
Цитаты
"Deep generative models exhibit promising performance across various tasks."
"OT-Flow leverages optimal transport theory to regularize CNFs."