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>.
