AR-VAE: Attribute-based Regularization of VAE Latent Spaces

About

AR-VAE is a type of Variational Auto-Encoder (VAE) which uses a new supervised training method to create structured latent spaces where specific continuous-valued attributes are forced to be encoded along specific dimensions of the latent space. This repository contains the source for training and evaluating the AR-VAE models across different image and music-based datasets.

The figure below shows the main idea behind the AR-VAE model.

This repository contains the source for training and evaluating the AR-VAE models across different image and music-based datasets. Please cite as follows if you are using the code in this repository in any manner.

Ashis Pati, Alexander Lerch. "Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders", Neural Computing and Applications. 2020.

@article{pati2020arvae,
  title={{Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders}},
  author={Pati, Ashis and Lerch, Alexander},
  journal={Neural Computing and Applications},
  issn={1433-3058},
  doi={10.1007/s00521-020-05270-2},
  url={https://doi.org/10.1007/s00521-020-05270-2},
  year={2020},
}

Results

Manipulation of Image Attributes

Manipulating attributes of 2-d shapes. Each row in the figure below represents a unique shape (from top to bottom): square, heart, ellipse. Each column corresponds to traversal along a regularized dimension which encodes a specific attribute (from left to right): Shape, Scale, Orientation, x-position, y-position.

Manipulating attributes of MNIST digits. Each row in the figure below represents a unique digit fronm 0 to 9. Each column corresponds to traversal along a regularized dimension which encodes a specific attribute (from left to right): Area, Length, Thickness, Slant, Width, Height.

Manipulation of Musical Attributes

Manipulating attributes of monophonic measures of music. In the figure below, measures on each staff line are generated by traversal along a regularized dimension which encodes a specific musical attribute (shown on the extreme left).

Piano roll version of the figure above is shown below. Plots on the right of each pianoroll show the progression of attribute values.

Installation and Usage

Install anaconda or miniconda by following the instruction here.

Create a new conda environment using the enviroment.yml file located in the root folder of this repository. The instructions for the same can be found here.

To install, either download / clone this repository. Open a new terminal, cd into the root folder of this repository and run the following command

python setup.py develop

Downloading Datasets:

Follow the steps below to download the different datasets:

• Create a folder named mnist_data in ar-vae/data/ directory. Download the morpho-mist data from this link and copy the folder named plain to the created mnist_data folder.
• Create a folder named dsprites in ar-vae/data/ directory. Clone this repository and copy its contents to the created dpsrites folder.
• Download the .zip file linked here. Unzip it and place the datasets and folk_raw_data folders in the ar-vae/data/ directory.

The final ar-vae/data/ directory should have the following sub-directories:

ar-vae/data
    --> dataloaders
        --> morphomnist
    --> datasets
    --> dpsprites
    --> folk_raw_data
    --> mnist_data
        --> plain

Downloading Models

Folow the steps below to download the provided pre-trained models:

• Create a folder named models/MnistRESNET in the root of this repository. Create the models folder if it doesn't exist.
• Download the MnistRESNET.pt model file and place it in the above folder.

Contents

The contents of this repository are as follows:

• data: contains the data and pytorch dataloaders for the different datasets.

• imagevae: module implementing the AR-VAE network and trainer for image datasets.

• measurevae: module implemening the AR-VAE network and trainer for music datasets.

• imagefader: module implementing the fader network and trainer for image datasets.

• figs: contains figures and plots for different experiments.

• utils: module with utility classes and methods for model, training, evaluation and plotting.

• other scripts to train / test the models