https://github.com/podgorskiy/gpnd

Generative Probabilistic Novelty Detection with Adversarial Autoencoders
https://github.com/podgorskiy/gpnd

aae adversarial-autoencoders adversarial-learning anomaly-detection autoencoder deep-learning deep-neural-networks deep-novelty-detection gan generative-adversarial-network machine-learning mnist nips-2018 novelty-detection novelty-detector pdf probability pytorch

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Generative Probabilistic Novelty Detection with Adversarial Autoencoders

• Host: GitHub
• URL: https://github.com/podgorskiy/gpnd
• Owner: podgorskiy
• Created: 2018-05-12T20:39:10.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-12-07T03:26:08.000Z (about 4 years ago)
• Last Synced: 2024-03-20T13:30:39.126Z (9 months ago)
• Topics: aae, adversarial-autoencoders, adversarial-learning, anomaly-detection, autoencoder, deep-learning, deep-neural-networks, deep-novelty-detection, gan, generative-adversarial-network, machine-learning, mnist, nips-2018, novelty-detection, novelty-detector, pdf, probability, pytorch
• Language: Python
• Homepage:
• Size: 5.71 MB
• Stars: 131
• Watchers: 11
• Forks: 31
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://arxiv.org/abs/1807.02588).

[NeurIPS Proceedings](https://papers.nips.cc/paper/7915-generative-probabilistic-novelty-detection-with-adversarial-autoencoders).

    @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](images/reconstruction_58.png?raw=true "MNIST Reconstruction")

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





![MNIST Reconstruction](images/sample_58.png?raw=true "MNIST Generation")

*MNIST Generation.*





![COIL100 Reconstruction](images/reconstruction_59_one.png?raw=true "COIL100 Reconstruction")

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





![COIL100 Generation](images/sample_59_one.png?raw=true "COIL100 Generation")

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





![COIL100 Reconstruction](images/reconstruction_59_seven.png?raw=true "COIL100 Reconstruction")

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





![COIL100 Generation](images/sample_59_seven.png?raw=true "COIL100 Generation")

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





![PDF](images/PDF.png?raw=true "PDF")

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