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Skip-VAE

Code for the paper:
Avoiding Latent Variable Collapse With Generative Skip Models
Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei.

Our code/data is based on the Semi-Amortized VAE repo. Please refer to the above repo for dependencies, data processing, etc.

Model

After downloading the sa-vae repo, copy these files to the sa-vae folder:

  • train_text_skip.py
  • models_text_skip.py
  • train_img_skip.py
  • models_img_skip.py

To run the text model:

python train_text_skip.py --train_file data/yahoo/yahoo-train.hdf5 --val_file data/yahoo/yahoo-val.hdf5 --gpu 1 --checkpoint_path model-path --skip 1 --model savae --svi_steps 20 --train_n2n 1

where model-path is the path to save the best model and the *.hdf5 files are obtained from running preprocess_text.py. You can specify which GPU to use by changing the input to the --gpu command.

To run the image model:

python train_img_skip.py --data_file data/omniglot/omniglot.pt --gpu 1 --checkpoint_path model-path --skip 1 --model savae --svi_steps 20 --train_n2n 1

License

MIT