AAAI 2022: Stationary diffusion state neural estimation

Related tags

Deep Learning SDSNE
Overview

Stationary Diffusion State Neural Estimation

Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, using a predefined graph limits their performance. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specifically design the Stationary Diffusion State Neural Estimation (SDSNE) to exploit multiview structural graph information for co-supervised learning. We explore how to design a graph neural network specially for unsupervised multiview learning and integrate multiple graphs into a unified consensus graph by a shared self-attentional module. The view-shared self-attentional module utilizes the graph structure to learn a view-consistent global graph. Meanwhile, instead of using auto-encoder in most unsupervised learning graph neural networks, SDSNE uses a co-supervised strategy with structure information to supervise the model learning. The co-supervised strategy as the loss function guides SDSNE in achieving the stationary state. Experiments on several multiview datasets demonstrate the effectiveness of SDSNE in terms of six clustering evaluation metrics.

Experiment

# SDSNE_km needs to use scikit-learn==0.23.1
conda install scikit-learn==0.23.1
bash demo_SDSNE.sh

Citation

We appreciate it if you cite the following paper:

@InProceedings{LiuAAAI2022,
  author =    {Chenghua Liu and Zhuolin Liao and Yixuan Ma and Kun Zhan},
  title =     {Stationary diffusion state neural estimation for multiview clustering},
  booktitle = {AAAI},
  year =      {2022},
  pages =     {1413--1421}
}

Contact

https://kunzhan.github.io/

If you have any questions, feel free to contact me. (Email: ice.echo#gmail.com)

You might also like...
Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

[AAAI22] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification We point out the overlooked unbiasedness in long-tailed clas

Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)
Attention-based Transformation from Latent Features to Point Clouds (AAAI 2022)

Attention-based Transformation from Latent Features to Point Clouds This repository contains a PyTorch implementation of the paper: Attention-based Tr

Code for One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)

One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022) Paper | Demo Requirements Python = 3.6 , Pytorch

Parallel and High-Fidelity Text-to-Lip Generation; AAAI 2022 ; Official code

Parallel and High-Fidelity Text-to-Lip Generation This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose P

 Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences 1. Introduction This project is for paper Model-free Vehicle Tracking and St

Propose a principled and practically effective framework for unsupervised accuracy estimation and error detection tasks with theoretical analysis and state-of-the-art performance.
Propose a principled and practically effective framework for unsupervised accuracy estimation and error detection tasks with theoretical analysis and state-of-the-art performance.

Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles This project is for the paper: Detecting Errors and Estimating

Implementation of the state of the art beat-detection, downbeat-detection and tempo-estimation model

The ISMIR 2020 Beat Detection, Downbeat Detection and Tempo Estimation Model Implementation. This is an implementation in TensorFlow to implement the

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

Continuous Diffusion Graph Neural Network

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Owner
绽琨
Kun Zhan received the BS and PhD degrees from Lanzhou University, China, in 2005 and 2010, respectively. Currently, He works in Lanzhou University.
绽琨
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi

Arthur Juliani 76 Jan 7, 2023
OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

OpenFace 2.2.0: a facial behavior analysis toolkit Over the past few years, there has been an increased interest in automatic facial behavior analysis

Tadas Baltrusaitis 5.8k Dec 31, 2022
Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models." (Gutmann and Hyvarinen, AISTATS 2010)

Noise Contrastive Estimation for pyTorch Overview This repository contains a re-implementation of the Noise Contrastive Estimation algorithm, implemen

Denis Emelin 42 Nov 24, 2022
Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

HackED 2022 Team 3IQ - 2022 Imposter Detector By Aneeljyot Alagh, Curtis Kan, Jo

Joshua Ji 3 Aug 20, 2022
Implementation of GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (ICLR 2022).

GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [OpenReview] [arXiv] [Code] The official implementation of GeoDiff: A Geome

Minkai Xu 155 Dec 26, 2022
Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".

multilingual-mrc-isdg Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph". This r

Liyan 5 Dec 7, 2022
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022) Prerequisite PyTorch >= 1.2.0 P

null 16 Dec 14, 2022
An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022

Dual Correlation Reduction Network An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022. Any

yueliu1999 109 Dec 23, 2022
Official repository of the AAAI'2022 paper "Contrast and Generation Make BART a Good Dialogue Emotion Recognizer"

CoG-BART Contrast and Generation Make BART a Good Dialogue Emotion Recognizer Quick Start: To run the model on test sets of four datasets, Download th

null 39 Dec 24, 2022