Denoising Normalizing Flow

Overview

Denoising Normalizing Flow

Christian Horvat and Jean-Pascal Pfister 2021

License: MIT

We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introducing the Denoising Normalizing Flow (DNF), a generative model able to

  1. approximate the data generating density p(x),
  2. generate new samples from p(x),
  3. infer low-dimensional latent variables.

As a classical NF degenerates for data living on a low-dimensional manifold embedded in high dimensions, the DNF inflates the manifold valued data using noise and learns a denoising mapping similar to DAE.

Related Work

The DNF is highly related to the Manifold Flow introduced by Johann Brehmer and Kyle Cramner. Also, our code is a cabon copy of their implementation with the following additions:

  1. The data can be inflated with Gaussian noise.
  2. We include the DNF as new mode for the ℳ-flow.
  3. New datasets, a thin spiral, a von Mises on a circle, and a mixture of von Mises on a sphere were added.
  4. A new folder, experiments/plots, for generating the images from the paper was added.
  5. A new folder, experiments/benchmarks, for benchmarking the DNF was added.
  6. The evaluate.py was modified and now includes the grid evaluation for the thin spiral and gan2d image manifold, the latent interpolations, the density estimation for the PAE, the latent density estimation on the thin spiral, and the KS statistics for the circle and sphere experiments.

The theoretical foundation of the DNF was developed in Density estimation on low-dimensional manifolds: an inflation-deflation approach.

Data sets

We trained the DNF and ℳ-flow on the following datasets:

Data set Data dimension Manifold dimension Arguments to train.py, and evaluate.py
Thin spiral 2 1 --dataset thin_spiral
2-D StyleGAN image manifold 64 x 64 x 3 2 --dataset gan2d
64-D StyleGAN image manifold 64 x 64 x 3 64 --dataset gan64d
CelebA-HQ 64 x 64 x 3 ? --dataset celeba

To use the model for your own data, you need to create a simulator (see experiments/datasets), and add it to experiments/datasets/init.py. If you have problems with that, please don't hesitate to contact us.

Benchmarks

We benchmark the DNF with the ℳ-flow, Probabilistic Auto Encoder (PAE), and InfoMax Variational Autoencoder. For that, we rely on the original implementations of those models, and modify them where appropriate, see experiments/benchmarks/vae and experiments/benchmarks/pae for more details.

Training & Evaluation

The configurations for the models and hyperparameter settings used in the paper can be found in experiments/configs.

Acknowledgements

We thank Johann Brehmer and Kyle Cramner for publishing their implementation of the Manifold Flow. For the experiments with the Probabilistic Auto-Encoder (V. Böhm and U. Seljak) and InfoMax Variational Autoencoder (A.L. Rezaabad, S. Vishwanath), we used the official implementations of these models. We thank these authors for this possibility.

Owner
CHrvt
CHrvt
This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

Hassan Shahzad 3 Oct 02, 2021
Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch

CutPaste CutPaste: image from paper Unofficial implementation of Google's "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization"

Lilit Yolyan 59 Nov 27, 2022
Molecular Sets (MOSES): A benchmarking platform for molecular generation models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

Neelesh C A 3 Oct 14, 2022
Contrastive Learning Inverts the Data Generating Process

Official code to reproduce the results and data presented in the paper Contrastive Learning Inverts the Data Generating Process.

71 Nov 25, 2022
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion" Coming soon, as soon as I finish a

Ziyao Zeng 14 Feb 26, 2022
ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.

VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built

remsens-lim 2 Apr 28, 2022
List some popular DeepFake models e.g. DeepFake, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, SimSwap, CihaNet, etc.

deepfake-models List some popular DeepFake models e.g. DeepFake, CihaNet, SimSwap, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, Si

Mingcan Xiang 100 Dec 17, 2022
Implementation of ReSeg using PyTorch

Implementation of ReSeg using PyTorch ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation Pascal-Part Annotations Pascal VOC 2010

Onur Kaplan 46 Nov 23, 2022
NovelD: A Simple yet Effective Exploration Criterion

NovelD: A Simple yet Effective Exploration Criterion Intro This is an implementation of the method proposed in NovelD: A Simple yet Effective Explorat

29 Dec 05, 2022
Epidemiology analysis package

zEpid zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The purpose of this library is

Paul Zivich 111 Jan 08, 2023
Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation

Implicit Internal Video Inpainting Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation paper | project

202 Dec 30, 2022
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
计算机视觉中用到的注意力模块和其他即插即用模块PyTorch Implementation Collection of Attention Module and Plug&Play Module

PyTorch实现多种计算机视觉中网络设计中用到的Attention机制,还收集了一些即插即用模块。由于能力有限精力有限,可能很多模块并没有包括进来,有任何的建议或者改进,可以提交issue或者进行PR。

PJDong 599 Dec 23, 2022
A repo for Causal Imitation Learning under Temporally Correlated Noise

CausIL A repo for Causal Imitation Learning under Temporally Correlated Noise. Running Experiments To re-train an expert, run: python experts/train_ex

Gokul Swamy 5 Nov 01, 2022
Minimalistic PyTorch training loop

Backbone for PyTorch training loop Will try to keep it minimalistic. pip install back from back import Bone Features Progress bar Checkpoints saving/l

Kashin 4 Jan 16, 2020
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

MixText This repo contains codes for the following paper: Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden

GT-SALT 309 Dec 12, 2022
Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm

Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neu

Filip Molcik 38 Dec 17, 2022
A list of all papers and resoureces on Semantic Segmentation

Semantic-Segmentation A list of all papers and resoureces on Semantic Segmentation. Dataset importance SemanticSegmentation_DL Some implementation of

Alan Tang 1.1k Dec 12, 2022
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:

Ravi Garg 227 Nov 28, 2022
Official implementation of "Learning Not to Reconstruct" (BMVC 2021)

Official PyTorch implementation of "Learning Not to Reconstruct Anomalies" This is the implementation of the paper "Learning Not to Reconstruct Anomal

Marcella Astrid 13 Dec 04, 2022