Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

Code for the paper Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models.

Requirements

See environment.yml. You can create the environment by running

conda env create -f environment.yml

Run VaGES-KSD

To compare VaGES-KSD with KSD and IS-KSD on the toy (checkerboard) dataset, run

python run_grbm_toy_ksd.py

Run VaGES-SM

To compare VaGES-SM with baselines on the toy (checkerboard) dataset, run

python run_grbm_toy.py

To compare VaGES-SM with BiSM on the Frey face dataset, run

python run_vagesdsm_grbm_freyface.py
python run_bidsm_grbm_freyface.py

To train a deep EBLVM on the cifar10 dataset, run

python run_vagesmdsm_eblvm_cifar10.py

To train a deep EBLVM on the celeba dataset, run

python run_vagesmdsm_eblvm_celeba.py

Run VaGES-Fisher

To estimate the Fisher divergence in GRBMs, run

python run_grbm_fisher.py

Remark

• The code will detect free GPUs and run on these GPUs. You can manually assign GPUs by modify the devices argument in functions in the above .py files.

• The downloaded dataset and running result will be saved to workspace directory by default.

Pre-trained models

Pre-trained models on cifar10 and celeba: link