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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.