Adversarial Maximal Mutual Information (AMMI)

This is a PyTorch implementation of AMMI [1]. Install dependencies (Python 3) and download data by running

pip install -r requirements.txt
./get_data.sh

Specifically, the experiments were run with Python 3.8.3 and PyTorch 1.5.3 using NVIDIA Quadro RTX 6000s (CUDA version 10.2).

Quick start

Unsupervised document hashing on Reuters using 16 bits

python ammi.py reuters16_ammi data/document_hashing/reuters.tfidf.mat --train --raw_prior

Output logged in file reuters16_ammi.log. You can simply switch the dataset to do predictive document hashing, for instance,

python ammi.py toy data/related_articles/article_pairs_tfidf_small.p --train --raw_prior --num_retrieve 10

The VAE and DVQ baselines can be run similarly by switching ammi.py with vae.py or dvq.py.

Reproducibility

See commands.txt for the hyperparameters used in the paper. They were optimized by random grid search on validation data, for instance

python ammi.py tmc64_ammi data/document_hashing/reuters.tfidf.mat --train --num_features 64 --num_runs 100 --cuda 
python ammi.py wdw128_ammi data/related_articles/article_pairs_tfidf.p --train --num_features 128 --num_runs 100 --cuda --num_workers 8

References

[1] Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information (Stratos and Wiseman, 2020)

@article{stratos2020learning,
  title={Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information},
  author={Stratos, Karl and Wiseman, Sam},
  journal={arXiv preprint arXiv:2004.03991},
  year={2020}
}