瞬时换元公式[1](Instantaneous Change of Variable,ICV) 是连续时间normalizing flow (CNF)中一个核心定理。最早由NeurIPS-2018 的best paper 之一的工作 Neural ODE提出。该定理给出了在一个常微分方程定义的变换下,概率密度的变换公式。该定理可以描述为: Theorem(Instantaneous Change of Variables)(Theorem 1 [1...
Specifically, we propose PoreFlow, a modular framework utilizing continuous normalizing flows (CNFs) for property-based microstructure generation. Our approach regularizes the CNF latent space by introducing target properties as a feature vector. Demonstrating the conditional generation process in our ...
There are two classes of normalizing flows: finite and continuous. A finite flow is defined as a composition of a finite number ofC1-diffeomorphisms:f=f1∘f2∘⋯∘fn. To make finite flows computationally tractable, eachfiis chosen to have some regularity properties such as a Jacobian wit...
PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flows. Guandao Yang*,Xun Huang*,Zekun Hao,Ming-Yu Liu,Serge Belongie,Bharath Hariharan(* equal contribution) ICCV 2019 (Oral) Introduction As 3D point clouds become the representation of choice for multiple vision and graphics applica...
Recent work has shown that Neural Ordinary Differential Equations (ODEs) can serve as generative models of images using the perspective of Continuous Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and invertible generation/density estimation. In this work we introduce a ...
Recent work has shown that Neural Ordinary Differential Equations (ODEs) can serve as generative models of images using the perspective of Continuous Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and invertible generation/density estimation. In this work we introduce a ...