anormaly-detection / gpnd

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

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.

The code is going to be cleaned up soon. The code for other datasets will be added soon.

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 from train_AAE.py, you need to call main function:

train_AAE.main(
  folding_id,
  inliner_classes,
  total_classes,
  folds=5
)

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.
• total_classes: Total count of classes.
• folds: Number of folds.

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

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

Set of arguments is the same.

Generated/Reconstructed images

MNIST Reconstruction. First raw - real image, second - reconstructed.

MNIST Generation.

COIL100 Reconstruction, single category. First raw - real image, second - reconstructed. Only 57 images were used for training.

COIL100 Generation. First raw - real image, second - reconstructed. Only 57 images were used for training.

COIL100 Reconstruction, 7 categories. First raw - real image, second - reconstructed. Only about 60 images per category were used for training

COIL100 Generation. First raw - real image, second - reconstructed. Only about 60 images per category were used for training.

PDF of the latent space for MNIST. Size of the latent space - 32