Journal Information
Neural Networks
http://www.journals.elsevier.com/neural-networks/
Impact Factor:
7.197
Publisher:
ELSEVIER
ISSN:
0893-6080
Viewed:
8992
Tracked:
23

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Call For Papers
Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters, reviews, and current opinions, as well as letters to the editor, book reviews, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.
Last updated by Dou Sun in 2018-09-23
Special Issues
Special Issue on Deep Neural Network Representation and Generative Adversarial Learning
Submission Date: 2019-09-30

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. Thisspecial 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. Main Topics include: 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.
Last updated by Dou Sun in 2019-06-09
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