trendingtechnology / ae_bakeoff

The Great Autoencoder Bake Off

The Great Autoencoder Bake Off

The companion repository to a post on my blog. It contains all you need to reproduce the results.

Features

Currently featured autoencoders:

• Shallow AE
• Deep (vanilla) AE
• Stacked AE
• Sparse AE
• Denoising AE
• VAE
• beta-VAE
• vq-VAE

They are evaluated on for the following tasks:

• Training Time
• Reconstruction quality
• Quality of decoded samples from the latent space (if possible)
• Quality of latent space interpolation
• Structure of the latent space visualized with UMAP
• ROC curve for anomaly detection with the reconstruction error
• Classification accuracy of a linear layer fitted on the autoencoder's features

Currently available datasets are:

• MNIST
• Fashion-MNIST (FMNIST)
• Kuzushiji-MNIST (KMNIST)

Installation

Clone the repository and create a new conda environment with:

conda create -n ae_bakeoff python=3.7
conda activate ae_bakeoff
conda install --file requirements.txt -c pytorch -c conda-forge

Verify the installation by running the tests:

cd ./tests
export PYTHONPATH="../src"
python -m unittest

Usage

To one-click reproduce the results for a dataset, call:

cd ./src
python reproduce.py --dataset <dataset> --batch_size 256 [--gpu]

If you want to run any specific experiment, call:

python run.py <autoencoder_type> --dataset <dataset> --batch_size 256 [--gpu] [--anomaly]

All experiments are recorded in the dicrectory ./logs/<dataset>.