PairNorm

Official pytorch source code for PairNorm paper (ICLR 2020)
This code requires pytorch_geometric>=1.3.2

usage

For SGC, we use original PairNorm. Notice norm_scale is data-dependent. One can choose it from {0.1, 1, 10, 50}.

python main.py --data cora --model SGC --nlayer 40 --missing_rate 100 --norm_mode PN --norm_scale 10

For GCN or GAT, we use PairNorm-SI or SCS.

python main.py --data cora --model DeepGCN --nlayer 10 --missing_rate 100 --norm_mode PN-SI --residual 0
python main.py --data cora --model DeepGAT --nlayer 10 --missing_rate 100 --norm_mode PN-SCS --residual 0

update: normalization and PN

we have found that PN works bad with symmetric normalized adjacency matrix, originally the experiments align with the paper used row-normalized adjacency matrix. What's more, we also found a small bug in the old experiments with using PN for GCN and GAT. The current version PN should works good for GCN and GAT also (haven't fully tested). Please start from using PN before testing PN-SI and PN-SCS.

For GCN or GAT, now using PN to start.

python main.py --data cora --model DeepGCN --nlayer 10 --missing_rate 100 --norm_mode PN --residual 0
python main.py --data cora --model DeepGAT --nlayer 10 --missing_rate 100 --norm_mode PN --residual 0

cite

If you use our code, please cite

@inproceedings{
zhao2020pairnorm,
title={PairNorm: Tackling Oversmoothing in {\{}GNN{\}}s},
author={Lingxiao Zhao and Leman Akoglu},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rkecl1rtwB}
}

Source code for PairNorm (ICLR 2020)

Related tags

Overview

PairNorm

usage

update: normalization and PN

cite

Owner

Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.

Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

The reference baseline of final exam for XMU machine learning course

Exploring Machine Learning Models for detecting anomalous behavior in credit-card transactions. It's crucial that credit-card companies are able to recognize fraudulent activity so that customers are not charged for items they didn't purchase.

Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Github Traffic Insights as Prometheus metrics.

Gated-Shape CNN for Semantic Segmentation (ICCV 2019)

Measuring Coding Challenge Competence With APPS

Single/multi view image(s) to voxel reconstruction using a recurrent neural network

Repository for open research on optimizers.

Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter

Generative Handwriting using LSTM Mixture Density Network with TensorFlow

All-in-one Docker container that allows a user to explore Nautobot in a lab environment.

Patch2Pix: Epipolar-Guided Pixel-Level Correspondences [CVPR2021]

A collection of models for image<->text generation in ACM MM 2021.

Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

functorch is a prototype of JAX-like composable function transforms for PyTorch.

Implementation of the Swin Transformer in PyTorch.

Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"