Normalising Flows
22 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Normalising Flows
Most implemented papers
Robust model training and generalisation with Studentising flows
simonalexanderson/StyleGestures
•
•
Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood.
Latent Transformations for Discrete-Data Normalising Flows
Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters.
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
L0SG/NanoFlow
•
•
NeurIPS 2020
Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis.
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
In this work, we propose a probabilistically principled general approach to SSL that considers the distribution over label predictions, for labels of different complexity, from "one-hot" vectors to binary vectors and images.
Sinusoidal Flow: A Fast Invertible Autoregressive Flow
Normalising flows offer a flexible way of modelling continuous probability distributions.
Bootstrap Your Flow
lollcat/FAB-2021
•
•
pproximateinference AABI Symposium 2022
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling.
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
vsimkus/variational-gibbs-inference
•
•
NeurIPS 2023
We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
akashsengupta1997/humaniflow
•
•
CVPR 2023
Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject.
Decorrelation using Optimal Transport
Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences.
NEnv: Neural Environment Maps for Global Illumination
seddi-research/NEnv
•
•
Computer Graphics Forum 2023
We propose NEnv, a deep-learning fully-differentiable method, capable of compressing and learning to sample from a single environment map.
