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
NeuralCompression is a Python repository dedicated to research of neural networks that compress data

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video c

Facebook Research 297 Jan 06, 2023
make ASCII Art by Deep Learning

DeepAA This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by NIPS 2017 Workshop,

OsciiArt 1.4k Dec 28, 2022
Py-faster-rcnn - Faster R-CNN (Python implementation)

py-faster-rcnn has been deprecated. Please see Detectron, which includes an implementation of Mask R-CNN. Disclaimer The official Faster R-CNN code (w

Ross Girshick 7.8k Jan 03, 2023
PG2Net: Personalized and Group PreferenceGuided Network for Next Place Prediction

PG2Net PG2Net:Personalized and Group Preference Guided Network for Next Place Prediction Datasets Experiment results on two Foursquare check-in datase

Urban Mobility 5 Dec 20, 2022
Image Recognition using Pytorch

PyTorch Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in

Sarat Chinni 1 Nov 02, 2021
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
Official Implementation (PyTorch) of "Point Cloud Augmentation with Weighted Local Transformations", ICCV 2021

PointWOLF: Point Cloud Augmentation with Weighted Local Transformations This repository is the implementation of PointWOLF(To appear). Sihyeon Kim1*,

MLV Lab (Machine Learning and Vision Lab at Korea University) 16 Nov 03, 2022
DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data.

DWIPrep: A Robust Preprocessing Pipeline for dMRI Data DWIPrep is a robust and easy-to-use pipeline for preprocessing of diverse dMRI data. The transp

Gal Ben-Zvi 1 Jan 09, 2023
Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss (ATVGnet)

Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss (ATVGnet) By Lele Chen , Ross K Maddox, Zhiyao Duan, Chenliang Xu. Unive

Lele Chen 218 Dec 27, 2022
Implementation of "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing".

DeepOrder Implementation of DeepOrder for the paper "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing". Project

6 Nov 07, 2022
A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch

Mixup: Beyond Empirical Risk Minimization in PyTorch This is an unofficial PyTorch implementation of mixup: Beyond Empirical Risk Minimization. The co

Harry Yang 121 Dec 17, 2022
Checkout some cool self-projects you can try your hands on to curb your boredom this December!

SoC-Winter Checkout some cool self-projects you can try your hands on to curb your boredom this December! These are short projects that you can do you

Web and Coding Club, IIT Bombay 29 Nov 08, 2022
EMNLP 2021 paper The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers.

Codebase for training transformers on systematic generalization datasets. The official repository for our EMNLP 2021 paper The Devil is in the Detail:

Csordás Róbert 57 Nov 21, 2022
Sign Language Transformers (CVPR'20)

Sign Language Transformers (CVPR'20) This repo contains the training and evaluation code for the paper Sign Language Transformers: Sign Language Trans

Necati Cihan Camgoz 164 Dec 30, 2022
Learning Continuous Image Representation with Local Implicit Image Function

LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo

Yinbo Chen 1k Dec 25, 2022
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme"

Dataset and Code for RealVSR Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme Xi Yang, Wangmeng Xiang,

Xi Yang 92 Jan 04, 2023
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links

LinkBERT: A Knowledgeable Language Model Pretrained with Document Links This repo provides the model, code & data of our paper: LinkBERT: Pretraining

Michihiro Yasunaga 264 Jan 01, 2023