Skip to content

Latest commit

 

History

History
61 lines (46 loc) · 2.21 KB

README.md

File metadata and controls

61 lines (46 loc) · 2.21 KB
Raw

WeLa-VAE: Learning alternative disentangled representations using weak labels

This is a minimilised version of the implementation of the respective manuscript.

The code was originally intended for research work. However, it is also useful for

  1. Providing simple keras implementations for famous VAE variants
    • BetaVAE (vanilla VAE if beta=1), TCVAE and DIP-VAE.
    • The implementation considers only dense layered encoder/decoder, but it can be easily extended to any other architecture (e.g. LSTM, Convolution).
  2. Showcasing the effect of these variants on learning disentangled representations through a simple, small toy-dataset. Training takes at most ~5min on CPU

Installation / Setup

Developed in Python 3.9. Python 3.7-3.10 should also work.

  1. Clone the repository
  2. cd into the repository, e.g. cd Desktop/repos/wela-vae or activate virtual environment, e.g. workon wela-vae
  3. Install requirements: pip install -r requirements/base.txt
  4. Create a python file local_settings.py inside lib, and set the following absolute paths:
    BLOBS_PATH = "desired/directory/to/store/blobs/data"
    RESULTS_PATH = "desired/directory/to/store/results"

Usage

Blobs dataset generation

    python -m lib.blob_generator

Training

Open lib/train_config.yaml and set the desired parameters for the model as well as for the training process. To train, for example, a TCVAE, run

    python -m lib.train --type tcvae

Other choices are betavae and dipvae. To train the WeLa variants, make sure that label_dim and gamma parameters are set in train_config.yaml and just use the --wela option in the command:

    python -m lib.train --type tcvae --wela

Results

Once the training has stopped (or interrupted by the user), two figures are generated:

  1. The qualitative evaluation figure in RESULTS_PATH/results/eval/<model type>/
  2. The training loss history in RESULTS_PATH/results/history/<model type>/

The name of the figures contain the type of the model, number of latent dimensions, number of label dimensions (for WeLa), weight initialization seed, as well as the variant's loss parameters (e.g., beta for TCVAE).