Generative Probabilistic Novelty Detection with Adversarial Autoencoders

2 minute read

Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto

Lane Department of Computer Science and Electrical Engineering, West Virginia University
Morgantown, WV 26508
{stpidhorskyi, ralmohse, daadjeroh, gidoretto} @mix.wvu.edu

The e-preprint of the article on arxiv.

NeurIPS Proceedings.

    @inproceedings{pidhorskyi2018generative,
      title={Generative probabilistic novelty detection with adversarial autoencoders},
      author={Pidhorskyi, Stanislav and Almohsen, Ranya and Doretto, Gianfranco},
      booktitle={Advances in neural information processing systems},
      pages={6822--6833},
      year={2018}
    }

Content

partition_mnist.py - code for preparing MNIST dataset.
train_AAE.py - code for training the autoencoder.
novelty_detector.py - code for running novelty detector
net.py - contains definitions of network architectures.

How to run

You will need to run partition_mnist.py first.

Then run schedule.py. It will run as many concurent experiments as many GPUs are available. Reusults will be written to results.csv file

Alternatively, you can call directly functions from train_AAE.py and novelty_detector.py

Train autoenctoder with train_AAE.py, you need to call train function:

    train_AAE.train(
      folding_id,
      inliner_classes,
      ic
    )

Args:

folding_id: Id of the fold. For MNIST, 5 folds are generated, so folding_id must be in range [0..5]
inliner_classes: List of classes considered inliers.
ic: inlier class set index (used to save model with unique filename).

After autoencoder was trained, from novelty_detector.py, you need to call main function:

    novelty_detector.main(
      folding_id,
      inliner_classes,
      total_classes,
      mul,
      folds=5
    )

folding_id: Id of the fold. For MNIST, 5 folds are generated, so folding_id must be in range [0..5]
inliner_classes: List of classes considered inliers.
ic: inlier class set index (used to save model with unique filename).
total_classes: Total count of classes (deprecated, moved to config).
mul: multiplier for power correction. Default value 0.2.
folds: Number of folds (deprecated, moved to config).