Autoencoder Experiments

Introduction

This repository contains source code of different types of autoencoder and experiments using autoencoders on different datasets.

Repository structure

.
├── autoencoders                      // main package, contains autoencoder classes
│   ├── __init__.py                   // 
│   ├── base_autoencoder.py           // base class for autoencoder
│   ├── adversarial_autoencoder.py    // Adversarial autoencoder class
│   ├── variational_autoencoder.py    // Variational autoencoder class
│   ├── denoising_autoencoder.py      // Denoising autoencoder class
│   ├── info_vae.py                   // Info variational autoencoder class
│   ├── ae_callbacks.py               // Callbacks class
│   └── utils.py                      //
├── docs
│   └── README.md                     // documentation, this file
├── experiments                       // script to run experiments with each type of autoencoder
│   ├── adversarial_ae
│   ├── denoising_ae
│   ├── info_vae
│   └── variational_autoencoder
├── setup.py                          // information for package installation
└── tests                             // test files
    └── autoencoders

Installation

Clone the repository

git clone https://github.com/KienMN/Autoencoder-Experiments.git

To install the autoencoder package, run the command

pip install git+https://github.com/KienMN/Autoencoder-Experiments.git

or

cd Autoencoder-Experiments
pip install -e .

Requrements

The code uses tensorflow 2 on GPU to run experiments efficiently.
See GPU support to install GPU with Tensorflow.

Usage

Experiments of each autoencoder are placed in the same folder. Hyperparameters and arguments are specified in file python script in each {autoencoder_folder}.

Run the experiments by the command:

python experiments/{autoencoder_folder}/{autoencoder_experiment}.py -d {dataset_name}

Citation

This source code is for the paper:

Mai Ngoc K., Hwang M. (2020) Finding the Best k for the Dimension of the Latent Space in Autoencoders. In: Nguyen N.T., Hoang B.H., Huynh C.P., Hwang D., Trawiński B., Vossen G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science, vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_35

@inproceedings{10.1007/978-3-030-63007-2_35,
author="Mai Ngoc, Kien and Hwang, Myunggwon",
title="Finding the Best k for the Dimension of the Latent Space in Autoencoders",
booktitle="Computational Collective Intelligence",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="453--464",
isbn="978-3-030-63007-2"
}

Reference

1. From Autoencoder to Beta-VAE: https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html
2. Adversarial autoencoder: https://github.com/Warvito/adversarial-autoencoder
3. InfoVAE: https://github.com/ShengjiaZhao/InfoVAE
4. Variatioanl autoencoder tutorial: https://www.tensorflow.org/tutorials/generative/cvae
5. Variational autoencoder: https://github.com/cdoersch/vae_tutorial