261 research outputs found
Deep Learning for Mobile Multimedia: A Survey
Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have proposed to drive evolutionary algorithms
using machine learning models. Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models. Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality. To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs). At each generation of the proposed algorithm, the parent
solutions are first classified into real and fake samples to train the GANs;
then the offspring solutions are sampled by the trained GANs. Thanks to the
powerful generative ability of the GANs, our proposed algorithm is capable of
generating promising offspring solutions in high-dimensional decision space
with limited training data. The proposed algorithm is tested on 10 benchmark
problems with up to 200 decision variables. Experimental results on these test
problems demonstrate the effectiveness of the proposed algorithm
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