Advances in Deep Learning

Machine-learning-based algorithms are widespread in several aspects of our daily life, from the advertising and logistics systems of corporations to the applications on our smartphones and cameras, with an ever-increasing number of devices including dedicated hardware. This growing deployment of machine-learning-based algorithms would not have been possible if not for the lightning-fast progress of the relevant research.

In recent years, a growing interest in deep learning approaches has been observed among the scientific community. These are a particular class of machine-learning techniques that allow an intelligent system to automatically learn a suitable data representation from the data themselves. This has been even more successful for multimedia applications, such as video and audio classification, due to the ability of deep-learning-based techniques to extract the implicit information of this kind of data. For instance, various deep learning classifiers have reached human performance in medical image classification for the recognition of a large number of diseases, narrowing the gap between the analytic capability of the machine and that of the human brain. Great improvements have also been achieved in the field of natural language processing, with techniques able to analyze and extract information from a text even when it lacks a predetermined form.

An even more interesting research trend is focusing on generative models: A completely novel deep learning approach that has shown the ability to learn a complex statistical distribution from its samples in an unsupervised manner. The aim of this approach is to train a neural network to generate new samples of the learned distribution. Generative models have demonstrated their effectiveness in different fields, from the generation of image and video that are marginally distinguishable from the original ones to text and speech automatic translation.

We encourage authors to submit original research articles, reviews, theoretical and critical perspectives, and viewpoint articles, on (but not limited to) the following topics:

- Convolutional neural networks;

- Recurrent neural networks;

- Generative neural network models;

- Comparison of neural networks and other methods;

- Multiscale multimedia analysis;

- Constrained learning approaches for critical applications;

- Predictive analysis;

- Developing new models for multimodal deep learning;

- Combining multiple deep learning models;

- Applications in vision, audio, speech, natural language processing, robotics, neuroscience, or any other field.

Dr. Diego Gragnaniello
Prof. Dr. Andrea Bottino
Dr. Sandro Cumani
Dr. Wonjoon Kim
Guest Editors

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