A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Jiaqi Ma

Last update: Oct 11, 2022

Related tags

Deep Learning G3NN

Overview

G3NN

This repo provides a pytorch implementation for the 4 instantiations of the flexible generative framework as described in the following paper:

A Flexible Generative Framework for Graph-based Semi-supervised Learning

Jiaqi Ma*, Weijing Tang*, Ji Zhu, and Qiaozhu Mei. NeurIPS 2019.

(*: equal contribution)

Requirements

See environment.yml. Run conda torch_env create -f environment.yml to install the required packages.

Run the code

Example: python main.py --model lsm_gcn --dataset cora

Cite

@inproceedings{ma2019flexible,
  title={A Flexible Generative Framework for Graph-based Semi-supervised Learning},
  author={Ma, Jiaqi and Tang, Weijing and Zhu, Ji and Mei, Qiaozhu},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3276--3285},
  year={2019}
}

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

This repo is the official implementation of "Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework". @inproceedings{zhou2021insta

34 Dec 31, 2022

CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

10 Oct 11, 2022

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

22 Nov 23, 2022

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

833 Dec 28, 2022

Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)

Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co

8 Oct 15, 2021

Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa

20 Dec 2, 2022

Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

LADA This repo contains codes for the following paper: Jiaao Chen*, Zhenghui Wang*, Ran Tian, Zichao Yang, Diyi Yang: Local Additivity Based Data Augm

36 Dec 2, 2022

Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022)

Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022) By Shilong Zhang*, Zhuoran Yu*, Liyang Liu*, Xinjiang Wang, Aojun Zhou,

129 Dec 24, 2022

Semi-supervised Learning for Sentiment Analysis

Neural-Semi-supervised-Learning-for-Text-Classification-Under-Large-Scale-Pretraining Code, models and Datasets for《Neural Semi-supervised Learning fo

47 Jan 1, 2023

Comments

Resolve package not found

您好，我用readme中的 conda torch_env create -f environment.yml不能安装，提示没有这个这个命令，只能用conda env create -f environment.yml，然后提示Resolve package not found，不知道z恩么解决呢？

opened by JialongWang1224 1
code error

Hi, I have ran the environment.yml file to create the env. However, I find this error ModuleNotFoundError: No module named 'torch_scatter.scatter_cuda'. I wonder if you have the solution of this error?

opened by Anooyman 1

Owner

Jiaqi Ma

GitHub https://arxiv.org/abs/1905.10769

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].

CGPN This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels]. Req

10 Sep 12, 2022

Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

2 Nov 19, 2021

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

2 Dec 17, 2021

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Related tags

Overview

G3NN

Requirements

Run the code

Cite

You might also like...

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)

Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022)

Semi-supervised Learning for Sentiment Analysis

Comments

Resolve package not found

code error

Owner

Jiaqi Ma

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].

Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

CVPR2022 paper "Dense Learning based Semi-Supervised Object Detection"

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Related tags

Overview

G3NN

Requirements

Run the code

Cite

You might also like...

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)

Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

Group R-CNN for Point-based Weakly Semi-supervised Object Detection (CVPR2022)

Semi-supervised Learning for Sentiment Analysis

Comments

Resolve package not found

code error

Owner

Jiaqi Ma

This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].

Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

CVPR2022 paper "Dense Learning based Semi-Supervised Object Detection"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.