smiler96 / Deep-Clustering

Unsupervised Deep Clustering

In the repository, the following UDC methods were implemented with pytorch

• DEC: Unsupervised Deep Embedding for Clustering Analysis - ICML2015
• DCEC: Deep Clustering with Convolutional Autoencoders - ICONIP2017

Reproduced Accuracy compared with original papers

Method	MNIST
	reproduced	paper
DEC	88.27	84.08
DCEC	87.41	88.97

Using Details

• DEC

python main.py --model DEC --dataset MNIST --n_clusters 10 --alpha 0.1 --batch_size 1024 --epochs 200 --pretrain --denoising
python main.py --model DEC --dataset MNIST --n_clusters 10 --alpha 0.1 --batch_size 1024 --epochs 500 --denoising

The AE was pretrained firstly and then finetuned with clustering together, alpha is the KL-divergence loss weight.

• DCEC

python main_conv.py --model DCEC --dataset MNIST --n_clusters 10 --alpha 0.1 --batch_size 1024 --epochs 200 --pretrain --denoising
python main_conv.py --model DCEC --dataset MNIST --n_clusters 10 --alpha 0.1 --batch_size 1024 --epochs 500 --denoising

The DCEC's network slightly differes from the one of the original paper.

Reference

• DCE-Pytorch: https://github.com/Deepayan137/DeepClustering
• DCEC-Keras: https://github.com/XifengGuo/DCEC