PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Overview

Completer: Incomplete Multi-view Clustering via Contrastive Prediction

This repo contains the code and data of the following paper accepted by CVPR 2021

COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

Requirements

pytorch==1.2.0

numpy>=1.19.1

scikit-learn>=0.23.2

munkres>=1.1.4

Configuration

The hyper-parameters, the training options (including the missing rate) are defined in configure.py.

Datasets

The Caltech101-20, LandUse-21, and Scene-15 datasets are placed in "data" folder. The NoisyMNIST dataset could be downloaded from cloud.

Usage

The code includes:

  • an example implementation of the model,
  • an example clustering task for different missing rates.
python run.py --dataset 0 --devices 0 --print_num 100 --test_time 5

You can get the following output:

Epoch : 100/500 ===> Reconstruction loss = 0.2819===> Reconstruction loss = 0.0320 ===> Dual prediction loss = 0.0199  ===> Contrastive loss = -4.4813e+02 ===> Loss = -4.4810e+02
view_concat {'kmeans': {'AMI': 0.5969, 'NMI': 0.6106, 'ARI': 0.6044, 'accuracy': 0.5813, 'precision': 0.4408, 'recall': 0.3835, 'f_measure': 0.3921}}
Epoch : 200/500 ===> Reconstruction loss = 0.2590===> Reconstruction loss = 0.0221 ===> Dual prediction loss = 0.0016  ===> Contrastive loss = -4.4987e+02 ===> Loss = -4.4984e+02
view_concat {'kmeans': {'AMI': 0.6575, 'NMI': 0.6691, 'ARI': 0.6974, 'accuracy': 0.6593, 'precision': 0.4551, 'recall': 0.4222, 'f_measure': 0.4096}}
Epoch : 300/500 ===> Reconstruction loss = 0.2450===> Reconstruction loss = 0.0207 ===> Dual prediction loss = 0.0011  ===> Contrastive loss = -4.5115e+02 ===> Loss = -4.5112e+02
view_concat {'kmeans': {'AMI': 0.6875, 'NMI': 0.6982, 'ARI': 0.8679, 'accuracy': 0.7439, 'precision': 0.4586, 'recall': 0.444, 'f_measure': 0.4217}}
Epoch : 400/500 ===> Reconstruction loss = 0.2391===> Reconstruction loss = 0.0210 ===> Dual prediction loss = 0.0007  ===> Contrastive loss = -4.5013e+02 ===> Loss = -4.5010e+02
view_concat {'kmeans': {'AMI': 0.692, 'NMI': 0.7027, 'ARI': 0.8736, 'accuracy': 0.7456, 'precision': 0.4601, 'recall': 0.4451, 'f_measure': 0.4257}}
Epoch : 500/500 ===> Reconstruction loss = 0.2281===> Reconstruction loss = 0.0187 ===> Dual prediction loss = 0.0008  ===> Contrastive loss = -4.5018e+02 ===> Loss = -4.5016e+02
view_concat {'kmeans': {'AMI': 0.6912, 'NMI': 0.7019, 'ARI': 0.8707, 'accuracy': 0.7464, 'precision': 0.4657, 'recall': 0.4464, 'f_measure': 0.4265}}

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{lin2021completer,
   title={COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction},
   author={Lin, Yijie and Gou, Yuanbiao and Liu, Zitao and Li, Boyun and Lv, Jiancheng and Peng, Xi},
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   month={June},
   year={2021}
}
Owner
XLearning Group
Xi Peng's XLearning Group
XLearning Group
AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages This repository contains the code for the pa

Kelechi 40 Nov 24, 2022
For the paper entitled ''A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining''

Summary This is the source code for the paper "A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining", which was accepted as fu

1 Nov 10, 2021
Code for "Human Pose Regression with Residual Log-likelihood Estimation", ICCV 2021 Oral

Human Pose Regression with Residual Log-likelihood Estimation [Paper] [arXiv] [Project Page] Human Pose Regression with Residual Log-likelihood Estima

JeffLi 347 Dec 24, 2022
Discord bot for notifying on github events

Git-Observer Discord bot for notifying on github events ⚠️ This bot is meant to write messages to only one channel (implementing this for multiple pro

ilu_vatar_ 0 Apr 19, 2022
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
Implementation of "Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner"

Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner This repository is the official implementation of Meta-rPPG: Remote Heart Ra

Eugene Lee 137 Dec 13, 2022
A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

ALLINONE-Det ALLINONE-Det is a general and strong 3D object detection codebase built on OpenPCDet, which supports more methods, datasets and tools (de

Michael.CV 5 Nov 03, 2022
Smart edu-autobooking - Johnson @ DMI-UNICT study room self-booking system

smart_edu-autobooking Sistema di autoprenotazione per l'aula studio [email protected]

Davide Carnemolla 17 Jun 20, 2022
Unsupervised Foreground Extraction via Deep Region Competition

Unsupervised Foreground Extraction via Deep Region Competition [Paper] [Code] The official code repository for NeurIPS 2021 paper "Unsupervised Foregr

28 Nov 06, 2022
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
Akshat Surolia 2 May 11, 2022
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Zicong Fan 40 Dec 22, 2022
Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

NVIDIA Research Projects 130 Jan 06, 2023
Sequence-to-Sequence learning using PyTorch

Seq2Seq in PyTorch This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train

Elad Hoffer 514 Nov 17, 2022
PyQt6 configuration in yaml format providing the most simple script.

PyamlQt(ぴゃむるきゅーと) PyQt6 configuration in yaml format providing the most simple script. Requirements yaml PyQt6, ( PyQt5 ) Installation pip install Pya

Ar-Ray 7 Aug 15, 2022
TAug :: Time Series Data Augmentation using Deep Generative Models

TAug :: Time Series Data Augmentation using Deep Generative Models Note!!! The package is under development so be careful for using in production! Fea

35 Dec 06, 2022
This is an early in-development version of training CLIP models with hivemind.

A transformer that does not hog your GPU memory This is an early in-development codebase: if you want a stable and documented hivemind codebase, look

<a href=[email protected]"> 4 Nov 06, 2022
This project aims to be a handler for input creation and running of multiple RICEWQ simulations.

What is autoRICEWQ? This project aims to be a handler for input creation and running of multiple RICEWQ simulations. What is RICEWQ? From the descript

Yass Fuentes 1 Feb 01, 2022
A python3 tool to take a 360 degree survey of the RF spectrum (hamlib + rotctld + RTL-SDR/HackRF)

RF Light House (rflh) A python script to use a rotor and a SDR device (RTL-SDR or HackRF One) to measure the RF level around and get a data set and be

Pavel Milanes (CO7WT) 11 Dec 13, 2022
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

RE2 This is a pytorch implementation of the ACL 2019 paper "Simple and Effective Text Matching with Richer Alignment Features". The original Tensorflo

287 Dec 21, 2022