β-VAE
Pytorch reproduction of two papers below:
- β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Higgins et al., ICLR, 2017
- Understanding disentangling in β-VAE, Burgess et al., arxiv:1804.03599, 2018
Dependencies
python 3.6.4
pytorch 0.3.1.post2
visdom
Datasets
Usage
initialize visdom
python -m visdom.server
you can reproduce results below by
sh run_celeba_H_beta10_z10.sh
sh run_celeba_H_beta10_z32.sh
sh run_3dchairs_H_beta4_z10.sh
sh run_3dchairs_H_beta4_z16.sh
sh run_dsprites_B_gamma100_z10.sh
or you can run your own experiments by setting parameters manually.
for objective and model arguments, you have two options H and B indicating methods proposed in Higgins et al. and Burgess et al., respectively.
arguments --C_max and --C_stop_iter should be set when --objective B. for further details, please refer to Burgess et al.
e.g.
python main.py --dataset 3DChairs --beta 4 --lr 1e-4 --z_dim 10 --objective H --model H --max_iter 1e6 ...
python main.py --dataset dsprites --gamma 1000 --C_max 25 --C_stop_iter 1e5 --lr 5e-4 --z_dim 10 --objective B --model B --max_iter 1e6 ...
check training process on the visdom server
localhost:8097
Results
3D Chairs
sh run_3dchairs_H_beta4_z10.sh
sh run_3dchairs_H_beta4_z16.sh
CelebA
sh run_celeba_H_beta10_z10.sh
sh run_celeba_H_beta10_z32.sh
dSprites
sh run_dsprites_B.sh
visdom line plot
latent traversal gif(--save_output True)
