Connectionists: CFP[deadline reminder]: Neural Networks journal special issue on GANs

[Ariel Ruiz-Garcia]

*TL;DR – CFP*:  Neural Networks journal special issue on GANs
*GUEST EDITORS*: Ariel Ruiz-Garcia, Jürgen Schmidhuber, Vasile Palade,
Clive Cheong Took, Danilo Mandic
*SUBMISSION DEADLINE*: 30th September 2019
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*CALL FOR PAPERS[deadline reminder]*: Neural Networks Journal

   *Special Issue on Deep Neural Network Representation and Generative
Adversarial Learning*

Generative Adversarial Networks (GANs) have proven to be efficient systems
for data generation. Their success is achieved by exploiting a minimax
learning concept, which has proved to be an effective paradigm in earlier
works, such as predictability minimization, in which two networks compete
with each other during the learning process. One of the main advantages of
GANs over other deep learning methods is their ability to generate new data
from noise, as well as their ability to virtually imitate any data
distribution. However, generating realistic data using GANs remains a
challenge, particularly when specific features are required; for example,
constraining the latent aggregate distribution space does not guarantee
that the generator will produce an image with a specific attribute. On the
other hand, new advancements in deep representation learning (RL) can help
improve the learning process in Generative Adversarial Learning (GAL). For
instance, RL can help address issues such as dataset bias and network
co-adaptation, and identify a set of features that are best suited for a
given task.

Despite their obvious advantages and their application to a wide range of
domains, GANs have yet to overcome several challenges. They often fail to
converge and are very sensitive to parameter and hyper-parameter
initialization. Simultaneous learning of a generator and a discriminator
network often results in overfitting. Moreover, the generator model is
prone to mode collapse, which results in failure to generate data with
several variations. Accordingly, new theoretical methods in deep RL and GAL
are required to improve the learning process and generalization performance
of GANs, as well as to yield new insights into how GANs learn data
distributions.

This special issue on Deep Neural Network Representation and Generative
Adversarial Learning invites researchers and practitioners to present novel
contributions addressing theoretical and practical aspects of deep
representation and generative adversarial learning. The special issue will
feature a collection of high quality theoretical articles for improving the
learning process and the generalization of generative neural networks.
State-of-the-art applications based on deep generative adversarial networks
are also very welcome. Topics of interest for this special issue include,
but are not limited to:

    Representation learning methods and theory;
    Adversarial representation learning for domain adaptation;
    Network interpretability in adversarial learning;
    Adversarial feature learning;
    RL and GAL for data augmentation and class imbalance;
    New GAN models and new GAN learning criteria;
    RL and GAL in classification;
    Adversarial reinforcement learning;
    GANs for noise reduction;
    Recurrent GAN models;
    GANs for imitation learning;
    GANs for image segmentation and image completion;
    GANs for image super-resolution;
    GANs for speech and audio processing
    GANs for object detection;
    GANs for Internet of Things;
    RL and GANs for image and video synthesis;
    RL and GANs for speech and audio synthesis;
    RL and GANs for text to audio or text to image synthesis;
    RL and GANs for inpainting and sketch to image;
    RL and GAL in neural machine translation;
    RL and GANs in other application domains.

*Important Dates:*

    30 September 2019 – Submission deadline
    31 December 2019 – First decision notification
    28 February 2020 – Revised version deadline
    30 April 2020 – Final decision notification
    July 2020 – Publication

*Guest Editors:*

Dr Ariel Ruiz-Garcia
Coventry University, UK
Email: ariel.9arcia at gmail.com

Professor Jürgen Schmidhuber
NNAISENSE,
Swiss AI Lab IDSIA,
USI & SUPSI, Switzerland
Email: juergen at idsia.ch

Dr Vasile Palade
Coventry University, UK
Email: vasile.palade at coventry.ac.uk

Dr Clive Cheong Took
Royal Holloway (University of London), UK
Email: Clive.CheongTook at rhul.ac.uk

Professor Danilo Mandic
Imperial College London, UK
Email: d.mandic at imperial.ac.uk

*Submission Procedure:*

Prospective authors should follow the standard author instructions for
Neural Networks, and submit manuscripts online at
http://ees.elsevier.com/neunet/. Authors should select “VSI:RL and GANs”
when they reach the “Article Type” step and the "Request Editor" step in
the submission process.

For any questions related to the special issue please email Dr Ariel
Ruiz-Garcia (ariel.9arcia at gmail.com)
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