Efficient Score Matching with Deep Equilibrium Layers

This code is based on the code of the following paper: Sliced Score Matching: A Scalable Approach to Density and Score Estimation](https://arxiv.org/abs/1905.07088), UAI 2019.

Dependencies

The following are packages needed for running this repo.

• PyTorch==1.0.1
• TensorFlow==1.12.0
• tqdm
• tensorboardX
• Scipy
• PyYAML

Running the experiments

python main.py --runner [runner name] --config [config file]

Here runner name is one of the following:

• DKEFRunner. This corresponds to experiments on deep kernel exponential families.
• NICERunner. This corresponds to the sanity check experiment of training a NICE model.
• VAERunner. Experiments on VAEs.
• WAERunner. Experiments on Wasserstein Auto-Encoders (WAEs).

and config file is the directory of some YAML file in configs/.

For example, if you want to train an implicit VAE of latent size 8 on MNIST with Sliced Score Matching, just run

python main.py --runner VAERunner --config vae/mnist_ssm_8.yml

For the NCSN model, use main.py is the file that you should run for both training and sampling. Execute python main.py --help to get its usage description:

usage: main.py [-h] --config CONFIG [--seed SEED] [--exp EXP] --doc DOC
               [--comment COMMENT] [--verbose VERBOSE] [--test] [--sample]
               [--fast_fid] [--resume_training] [-i IMAGE_FOLDER] [--ni]

The DEQ models will run by default. If you wanna run other models, just open the config file and set deq (or fix_nn) as False.