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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.

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).

Generated/Reconstructed images

MNIST Reconstruction

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

MNIST Reconstruction

MNIST Generation.

COIL100 Reconstruction

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

COIL100 Generation

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

COIL100 Reconstruction

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

COIL100 Generation

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

PDF

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