Connectionists: [CFP extended deadline] IJCNN-WCCI2020: Special Session on GANs

[Ariel Ruiz-Garcia]

*TL;DR – CFP*:  IJCNN special session on GANs
*GUEST EDITORS*: Ariel Ruiz-Garcia, Vasile Palade, Jürgen Schmidhuber,
Clive Cheong Took, Danilo Mandic
*SUBMISSION DEADLINE(extended)*: 30th January 2020
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*CALL FOR PAPERS*
Special Session on *Deep and Generative Adversarial Learning*

IEEE World Congress on Computational Intelligence (IEEE WCCI 2020)
International Joint Conference on Neural Networks
July 19-24, 2020 Glasgow, Scotland, UK

*Abstract:*
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 adversarial curiosity (1990) and predictability minimization
(1991), 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 session on Deep Representation and Generative Adversarial
Learning aims to bring together researchers and practitioners to discuss
and present their findings on RL and GANs. The special session will invite
novel contributions on new theoretical methods and applications of RL and
GANs.

*Topics of interest for this special session 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:*
15 January 2020 – Submission deadline
15 March 2020 – Paper acceptance notification
15 April 2020 – Final paper submission and early registration deadline
19-24 July 2020 – Paper presentations at IEEE WCCI 2020

*Organizers*:
Dr Ariel Ruiz-Garcia
Arm Ltd, UK
Email: ariel.9arcia at gmail.com

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

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

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 author instructions for IEEE IJCNN
found at https://wcci2020.org/submissions/ and submit manuscripts online at
https://ieee-cis.org/conferences/ijcnn2020/upload.php Authors should select
'S35. Deep and Generative Adversarial Learning” when they reach the “Main
research topic” step and leave “Additional research topics” empty during
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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