DCCM

This repository is a PyTorch implementation for Deep Comprehensive Correlation Mining for Image Clustering (accepted to ICCV 2019) at https://arxiv.org/abs/1904.06925?context=cs.CV

by Jianlong Wu*, Keyu Long*, Fei Wang, Chen Qian, Cheng Li, Zhouchen Lin and Hongbin Zha.

citation

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

@inproceedings{DCCM,
    author={Wu, Jianlong and Long, Keyu and Wang, Fei and Qian, Chen and Li, Cheng and Lin, Zhouchen and Zha, Hongbin},
    title={Deep Comprehensive Correlation Mining for Image Clustering},
    booktitle={International Conference on Computer Vision},   
    year={2019},   
}

Table of contents

• Introduction
• Usage
• Requirments
• Comparisons with SOTAs
• Reference github repos

Introduction

Figure 1. The pipeline of the proposed DCCM.

Usage

To train with CIFAR10/100 datasets, try:

$ python main.py --config cfgs/cifar10.yaml
$ python main.py --config cfgs/cifar100.yaml

To resume with a certain checkpoint , try:

$ python main.py --config cfgs/xx.yaml --resume xxx.ckpt

Parameters and datapaths can be modified in the config files.

Note that we use meta-files (examples could be found in the folder 'meta') to load data.

Requirments

• a Python installation version 3.6.5
• a Pytorch installation version 0.4.1
• a Keras installation version 2.0.2
• download the image dataset and stored according to the meta-files

Please note that all reported performance are tested under this environment.

Comparisons with SOTAs

Table 1. Clustering performance of different methods on six challenging datasets.

Reference Github Repos

• vector-1127/DAC: original implemention of DAC
• rdevon/DIM: original implementation of DIM
• 11-626/Deep-INFOMAX: PyTorch implementation of DIM

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