Official Pytorch Implementation for Gacs-Korner Common Information Variational Autoencoder (NeurIPS 2023) .
The main training script is main.py.
For example, to train on multiple views of dsprites:
python main.py cvae_ddsprites_randSample_klqq=0.1_klu=10_epoch=70_batch=128_z=8_zu=3_seed=0 -s 0 -d ddsprites -m Doubleburgess -md Doubleburgess -l CVAE --lr 0.001 -b 128 -e 70 -z 8 -zu 3 --gamma-klu 10 --gamma-klqq 0.1
These commands (and the others used in the paper) are generated in scripts/generate_paper_commands.py.
The main evaluation script is main_eval.py. For example, run:
python main_eval.py --name cvae_ddsprites_randSample_klqq=0.1_klu=10_epoch=70_batch=128_z=8_zu=3_seed=0 --dataset ddsprites --nu 3 --nz 8 --num-factors 5
Note that --num-factors 5 corresponds to the number of ground-truth latent factors.
The main visualization script is main_viz.py. To generate traversals, run:
python main_viz.py cvae_ddsprites_randSample_klqq=0.1_klu=10_epoch=70_batch=128_z=8_zu=3_seed=0 traversals -r 8
Plotting scripts (which show how to load logged data) begin with plot_*. For example, run:
python plot_disentanglement_hinton.py --result-dir results --name cvae_ddsprites_randSample_klqq=0.1_klu=10_epoch=70_batch=128_z=8_zu=3_seed=0
Visualizations, plots, and metrics will be in results/{name}
- python 3.6+
- torch
- torchvision
- scipy
- seaborn (for plotting)
This repository builds off: https://github.com/YannDubs/disentangling-vae
If you find this useful for your work, please consider citing
@inproceedings{
kleinman2023gacskorner,
title={Gacs-Korner Common Information Variational Autoencoder},
author={Michael Kleinman and Alessandro Achille and Stefano Soatto and Jonathan Kao},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=e4XidX6AHd}
}