Package for working with hypernetworks in PyTorch.

Overview

Hypernetworks for PyTorch

This package contains utilities that make it easy to work with hypernetworks in PyTorch.

Installation

You can either install the latest package version via

python3 -m pip install hypnettorch

or, you directly install the current sources

python3 -m pip install git+https://github.com/chrhenning/hypnettorch

Installation for developers

If you actively develop the package, it is easiest to install it in development mode, such that all changes that are done to source files are directly visible when you use the package.

Clone the repository to a location of your choice

git clone https://github.com/chrhenning/hypnettorch.git

and move inside the cloned repo

cd ./hypnettorch

Now, you can simply install the package in editable mode, which will ensure that you can easily update the package sources (cf. development mode)

pip3 install --editable . --user

Since the package was installed in editable mode, you can always update the sources simply by pulling the most recent code

git pull

You can uninstall the package at any point by running python3 setup.py develop -u.

Usage

The basic functionalities of the package are quite intuitive and easy to use, e.g.,

from hypnettorch.mnets import MLP
net = MLP()

There are several tutorials. Check out the getting started tutorial when working with hypnettorch for the first time.

Documentation

The documentation can be found here.

Note for developers

The documentation can be build using

python3 setup.py build_sphinx

and opened via the file index.html.

Citation

When using this package in your research project, please consider citing one of our papers for which this package has been developed.

@inproceedings{oshg2019hypercl,
title={Continual learning with hypernetworks},
author={Johannes von Oswald and Christian Henning and Jo{\~a}o Sacramento and Benjamin F. Grewe},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://arxiv.org/abs/1906.00695}
}
@inproceedings{ehret2020recurrenthypercl,
  title={Continual Learning in Recurrent Neural Networks},
  author={Benjamin Ehret and Christian Henning and Maria R. Cervera and Alexander Meulemans and Johannes von Oswald and Benjamin F. Grewe},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://arxiv.org/abs/2006.12109}
}
@inproceedings{posterior:replay:2021:henning:cervera,
title={Posterior Meta-Replay for Continual Learning}, 
      author={Christian Henning and Maria R. Cervera and Francesco D'Angelo and Johannes von Oswald and Regina Traber and Benjamin Ehret and Seijin Kobayashi and João Sacramento and Benjamin F. Grewe},
booktitle={Conference on Neural Information Processing Systems},
year={2021},
url={https://arxiv.org/abs/2103.01133}
}
Owner
Christian Henning
Machine Learning Researcher
Christian Henning
CountDown to New Year and shoot fireworks

CountDown and Shoot Fireworks About App This is an small application make you re

5 Dec 31, 2022
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model Edresson Casanova, Christopher Shulby, Eren Gölge, Nicolas Michael Müller, Frede

Edresson Casanova 92 Dec 09, 2022
Visual Adversarial Imitation Learning using Variational Models (VMAIL)

Visual Adversarial Imitation Learning using Variational Models (VMAIL) This is the official implementation of the NeurIPS 2021 paper. Project website

14 Nov 18, 2022
Attention-guided gan for synthesizing IR images

SI-AGAN Attention-guided gan for synthesizing IR images This repository contains the Tensorflow code for "Pedestrian Gender Recognition by Style Trans

1 Oct 25, 2021
A lightweight deep network for fast and accurate optical flow estimation.

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation The official PyTorch implementation of FastFlowNet (ICRA 2021). Authors: Lingtong

Tone 161 Jan 03, 2023
Flask101 - FullStack Web Development with Python & JS - From TAQWA

Task: Create a CLI Calculator Step 0: Creating Virtual Environment $ python -m

Hossain Foysal 1 May 31, 2022
Multi-Glimpse Network With Python

Multi-Glimpse Network Our code requires Python ≥ 3.8 Installation For example, venv + pip: $ python3 -m venv env $ source env/bin/activate (env) $ pyt

9 May 10, 2022
Virtual Dance Reality Stage is a feature that offers you to share a stage with another user virtually.

Virtual Dance Reality Stage is a feature that offers you to share a stage with another user virtually. It uses the concept of Image Background Removal using DeepLab Architecture (based on Semantic Se

Devashi Choudhary 5 Aug 24, 2022
The FIRST GANs-based omics-to-omics translation framework

OmiTrans Please also have a look at our multi-omics multi-task DL freamwork 👀 : OmiEmbed The FIRST GANs-based omics-to-omics translation framework Xi

Xiaoyu Zhang 6 Dec 14, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Apple 3k Jan 08, 2023
Info and sample codes for "NTU RGB+D Action Recognition Dataset"

"NTU RGB+D" Action Recognition Dataset "NTU RGB+D 120" Action Recognition Dataset "NTU RGB+D" is a large-scale dataset for human action recognition. I

Amir Shahroudy 578 Dec 30, 2022
A Python package for causal inference using Synthetic Controls

Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim

Oscar Engelbrektson 107 Dec 28, 2022
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas

IDRL 21 Dec 15, 2022
Contenido del curso Bases de datos del DCC PUC versión 2021-2

IIC2413 - Bases de Datos Tabla de contenidos Equipo Profesores Ayudantes Contenidos Calendario Evaluaciones Resumen de notas Foro Política de integrid

54 Nov 23, 2022
Predicting a person's gender based on their weight and height

Logistic Regression Advanced Case Study Gender Classification: Predicting a person's gender based on their weight and height 1. Introduction We turn o

1 Feb 01, 2022
A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.

How to apply? Create your config.ini file following the example provided in config.ini Choose one of the options below to run: Run with Python3 pip in

Scientific Data Management Group 3 Jun 23, 2022
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

Benedek Rozemberczki 251 Nov 30, 2022
FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning. ICCV, 2021.

FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning PyTorch implementation for the paper: FACIAL: Synthesizing Dynamic Talking

226 Jan 08, 2023
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

Paul Gavrikov 18 Dec 30, 2022