Information-Theoretic Methods for Deep Learning Based Data Acquisition, Analysis and Security

Dear Colleagues,

The recent advancement of machine learning techniques in general and deep learning (DL) in particular recalls a necessity to carefully rethink many traditional approaches in data acquisition, analysis, processing, and security. At the same time, deep learning, as a glorified signal processing tool, lacks a solid information-theoretical basis and strong connections with the fundamental information-theoretic results in channel and source coding, hypothesis testing, estimation, and security. The goal of this Special Issue is to link deep learning techniques with information theory and thus create a basis for theoretically explainable machine learning and interpretable deep learning solutions.

We would like to welcome original works addressing the following:

• New methods for data acquisition, including sampling, when the acquisition/imaging operator is optimized on the statistics of training data and deep reconstruction algorithms (e.g., DL for learning optimized generational methods, DL-based adaptation of sensor planning for high resolution image acquisition and reconstruction in sensor networks); a special interest is in new approaches establishing the fundamental links between information-theoretic limits and characteristics of imaging systems coupled with reconstruction algorithms considered as optimized encoder–decoder pairs. Contributions are welcome to cover the broad spectrum of problems, such as restoration from blurred images and videos, reconstruction from sparsely sampled data in transform domains, demosaicing, super-resolution, and inpainting, etc;
• New methods of data analysis bridging information-theoretic methods with deep learning systems and promoting explainable machine learning, especially in unsupervised settings; this concerns the information-theoretic analysis of learned features and filters. One of the main goals is to better characterize the impact of the training data and clearly understand which portion of training data contributes to the success of the derived solutions. Possible topics include the exploration of the link between DL and variational inference, the information bottleneck paradigm, and anomaly detection;
• New approaches to secure machine learning to reduce vulnerability to adversarial attacks, thus bridging information-theoretic and cryptographic principles with machine learning; in this way, it is possible to characterize improved security in terms of an ‘information’ advantage of the defender over the attacker. Particular interest is also devoted to approaches pertaining to the development of information theoretical methods to measure the vulnerability of systems to attack;
• New techniques for privacy preserving learning extending the limits of current centralized architectures (based on a single classifier having the access to the training data from all classes) to distributed and federated architectures with a partial and limited access to training data.

This Special Issue should serve as a platform for multi-disciplinary researchers interested in sharing their results with other communities using similar techniques. All submitted manuscripts will be subject to peer review, and accepted papers will be available via open access. We welcome the submission of extended conference papers with a clear justification of all extensions with respect to previously published works.

Prof. Dr. Slava Voloshynovskiy
Dr. Benedetta Tondi
Guest Editors

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