Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Design of a Novel Efficient High-Gain Ultra-Wide-Band Slotted H-Shaped Printed 2×1 Array Antenna for Millimeter-Wave Applications with Improvement of Bandwidth and Gain via the Feed Line and Elliptical Edges

This paper describes design procedure of a high-performance miniaturized antenna with an array configuration, which contributes to enhancing the communication system’s performance. The basic antenna features a compact size (6 x 6) mm2, and its single element is an H-shaped slotted patch printed on the top side of a Rogers RT5880 substrate, with a relative permittivity and thickness of 2.2 and 0.3 mm, respectively. The edge-to-edge distance of the 2 × 1 array antenna is 9 x 14 mm2, and the isolation between its radiation elements is 4.5 mm. To increase the capabilities of the antenna in terms of gain and bandwidth, we proceeded to use the 2 × 1 array configuration and then optimized the model via either the width of the feed line or the elliptical edges of the patch. The miniaturized array antenna achieved a peak gain of 12.56 dB, a directivity of 13.11 dBi, and a return loss of -47.52 dB at a resonance frequency of 91.5 GHz, with a radiation efficiency of more than 91% over an operating bandwidth of 15.83 GHz, ranging from 79.7 GHz to 95.6 GHz. The design and simulation results of the proposed antenna were obtained using the CST Studio software

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges