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Human Image Synthesis From Reflected Radio Waves

Human Image Synthesis From Reflected Radio Waves
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Generative Adversarial Networks Gans , Generative Adversarial Networks , Technology Of China , University Of Electronic Science , Based Human Synthesis , Electronic Science , Modulated Continuous Wave ,

NVIDIA's new AI eats words, spits out photos and feels borderline magical

NVIDIA has as generative art system that uses AI to turn words into visually spectacular works of art. This isn't the first time this sort of concept has been ....

Generative Adversarial Networks , Cornell University , Deep Dream Generator , Google Photos ,

"A cost-sensitive deep learning based approach for network traffic clas" by Akbar Telikani, Amir H. Gandomi et al.

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ ....

Class Imbalance , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Ncrypted Traffic Classification , Generative Adversarial Networks , Intrusion Detection , Task Analysis ,

"Determining learning direction via multi-controller model for stably s" by Yi Fan, Quoqiang Zhou et al.

The data generated by Generative Adversarial Network (GAN) inevitably contains noise, which can be reduced by searching and optimizing the architecture of GAN. To search for generative adversarial networks architectures stably, a neural architecture search (NAS) method, StableAutoGAN, is proposed based on the existing algorithm, AutoGAN. The stability of conventional reinforcement learning (RL)-based NAS methods for GAN is adversely influenced by the uncertainty of direction, where the controller will go forward once receiving inaccurate rewards. In StableAutoGAN, a multi-controller model is employed to mitigate this problem via comparing the performance of controllers after receiving rewards. During the search process, each controller independently learns the sampling policy. Meanwhile, the learning effect is measured by the credibility score, which further determines the usage of controllers. Our experiments show that the standard deviation of Frchet Inception Distance (FID) scores o ....

Generative Adversarial Network , Frchet Inception Distance , Generative Adversarial Networks , Ulti Controller Model , Neural Architecture Search ,

Kongsberg Geospatial, SFL Scientific Introduce AI-Driven Technology to Identify and Display Chemical Threats Using Drones

Ottawa, ON (PRWEB) November 03, 2021 Kongsberg Geospatial and SFL Scientific, a Boston-based data science consulting company announced that they will be ....

United States , Rex Hayes , Michael Segala , Generative Adversarial Networks Gans , Association For Unmanned Vehicle Systems International , Kongsberg Geospatial , Artificial Intelligence Driven , Unmanned Aerial Vehicles , Unmanned Vehicle Systems International , Generative Adversarial Networks , Graph Deep Learning , Beyond Visual Line Of Sight , Unmanned Systems , Kongsberg Defence , Press Release ,