Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning

Installation

1. Repository setup:

• $ git clone https://github.com/intelligolabs/Detaux
• $ cd Detaux
• $ git clone https://github.com/google-research/disentanglement_lib.git
• $ mv disentanglement_lib disentanglement_library
• $ cp -r disentanglement_lib_patch/* disentanglement_library/disentanglement_lib/
• Download 3dshapes.h5 from https://console.cloud.google.com/storage/browser/3d-shapes;tab=objects?prefix=&forceOnObjectsSortingFiltering=false

2. Conda enviroment setup:

• $ conda create -n detaux python=3.7
• $ conda activate detaux
• $ python -m pip install pytorch-lightning==1.9.4
• $ cd disentanglement_library/
• $ python -m pip install -v -e .
• $ python -m pip install tensorflow-gpu==1.14
• $ python -m pip install --upgrade tensorboard
• $ cd ../
• $ python -m pip install wandb
• $ pip install torchvision

Run Detaux

1. To run the disentanglement part, use the file detaux.py. In particular, launch_dis.sh it contains one example of a launch script that you can use to modify the default configuration directly.
2. To run the clustering part, use the file clustering.py.
3. Finally, with the file aux_learning.py, you will be able to perform the auxiliary learning phase with the new labels discovered in step 2.

Citation

If you use Detaux, please, cite the following paper:

@article{skenderi2023disentangled,
  title={Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning},
  author={Skenderi, Geri and Capogrosso, Luigi and Toaiari, Andrea and Denitto, Matteo and Fummi, Franco and Melzi, Simone and Cristani, Marco},
  journal={arXiv preprint arXiv:2310.09278},
  year={2023}
}

Credits

We want to thank Marco Fumero for the repository PMPdisentanglement, which provides us with the scripts used to manage the disentanglement part.