rxlgq / vade

Python code for paper - Variational Deep Embedding : A Generative Approach to Clustering

VaDE

This repository contains the Python implementation for our generative clustering method VaDE.

Further details about VaDE can be found in our paper:

Variational Deep Embedding : A Generative Approach to Clustering Requirements

• Python-3.4.4
• keras-1.1.0
• scikit-learn-1.17.1

Replace keras/engine/training.py by training.py in this repository!!

(The modification version of keras/engine/training.py enables the simultaneous updating of the gmm parameters and the network parameters in our model.)

Usage

• To train the VaDE model on the MNIST, Reuters, HHAR datasets:

python ./VaDE.py db

db can be one of mnist,reuters10k,har.

• To achieve the 94.46% clustering accuracy on the MNIST dataset and generate the class-specified digits (Note that: unlike Conditional GAN, we do not use any supervised information during training):

python ./VaDE_test_mnist.py

• To achieve the 79.38% clustering accuracy on the Reuters(685K) dataset:

cd $VaDE_ROOT/dataset/reuters
./get_data.sh
cd $VaDE_ROOT
python ./VaDE_test_reuters_all.py

Note: the data preprocessing code for the Reuters dataset is taken from (https://github.com/piiswrong/dec).

Face generation by VaDE on CelebA

(DCGAN-like network architecture)

• Attribute-conditioned generation (sampled from each cluster) without using any supervised information

1-6 rows: 1.black/short hair, man; 2.black/long hair, woman; 3.gold/long hair, woman; 4.bald, sunglasses, man; 5.left side face, woman; 6.right side face, woman;

• Interpolation between cluster centers in latent space

• Vector arithmetic in latent space：right + left = front