PyTorch implementation of normalizing flow models
Real NVP PyTorch a Minimal Working Example | Normalizing Flow
a deep recurrent model for exchangeable data
Implementing Real-NVP from Scratch in Pytorch
Normalizing flows for density estimation with built-in support for sampling.
An Invertible Neural Network using Variational-Inference to estimate the model uncertainty
This repository implements REAL NVP (Real-valued Non-Volume Preserving), a normalizing flow model that uses coupling layers to transform complex data into a simpler distribution, like a Gaussian. It efficiently reconstructs realistic data from a normal distribution without iterative steps, making inference fast.
Implementation of various generative models for homework assignments for course Generative Neural Networks for the Sciences.
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