Null-sampling for Interpretable and Fair Representations

Implementation of our paper Null-sampling for Interpretable and Fair Representations.

Requirements

Python 3.8 (or higher)

Installing dependencies

The dependencies are listed in the setup.py. To install them all, do

pip install -e .

Running the code

Training of the CelebA cFlow model can be reproduced for CelebA and cMNIST, respectively, with the folowing commands

python start_inn.py --dataset celeba --levels 3 --level-depth 32 --glow True --reshape-method squeeze --autoencode False --input-noise True --quant-level 5 --use-wandb True --factor-splits 0=0.5 1=0.5 --train-on-recon False --recon-detach False --batch-size 32 --nll-weight 1 --pred-s-weight 1e-2 --zs-frac 0.001 --coupling-channels 512 --super-val True --super-val-freq 10 --val-freq 1 --task-mixing 0.5 --gpu 0 --num-discs 10 --disc-channels 512 --data-split-seed 42 --iters 76000

python start_inn.py --dataset cmnist --levels 3 --level-depth 24 --glow True --reshape-method squeeze --autoencode False --input-noise True --quant-level 5 --use-wandb True --factor-splits 0=0.5 1=0.5 --train-on-recon False --recon-detach False --batch-size 256 --test-batch-size 512 --nll-weight 1 --pred-s-weight 1e-2 --zs-frac 0.002 --coupling-channels 512 --super-val True --super-val-freq 5 --val-freq 1 --task-mixing 0 --gpu 0 --num-discs 1 --disc-channels 512 --level-depth 24 --num-discs 3

Citing This Work

@InProceedings{KehBarThoQua20,
  author    = {Kehrenberg, Thomas and 
               Bartlett, Myles and 
               Thomas, Oliver and 
               Quadrianto, Novi},
  editor    = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
  title     = {Null-Sampling for Interpretable and Fair Representations},
  booktitle = {Computer Vision -- ECCV 2020},
  year      = {2020},
  publisher = {Springer International Publishing},
  address   = {Cham},
  pages     = {565--580},
  isbn      = {978-3-030-58574-7}
}