On-device neural network training has emerged as a key enabler for privacy-preserving and personalized data processing at the edge. However, the deployment of Few-Shot Learning …
Sensors capture large volumes of data containing both critical and redundant information for neural network (NN) inference. However, limited bandwidth and energy efficiency at the …
In this letter, we predict the locations as probability distributions for the tasks of image object detection. We adopt the Kullback-Leibler divergences as the regression losses to …
Convolutional neural network (CNN), one of the branches of deep neural networks, has been widely used in image recognition, natural language processing, and other related fields …
Compared with the state-of-the-art architectures, using the 3D point cloud as the input of the 2D convolutional neural network without preprocessing will restrict the feature …
Recently, studies on single image super-resolution using Deep Convolutional Neural Networks (DCNN) have been demonstrated to have made outstanding progress over conventional …
Convolutional neural networks (CNNs) have been widely applied in super-resolution (SR) and other image restoration tasks. Recently, Hinton et al. proposed capsule neural networks …
Two-way full-duplex (TWFD) transmissions in cooperative amplify-and-forward (AF) relaying systems provide a promising way of communications with the improved spectral efficiency by …
In this paper, we propose to improve the performance of the rate-control mechanism in video encoders by adaptively adjusting the Lagrange multiplier λ based on the stochastic …
A special type of cooperative wireless transmission scheme is proposed for the broadcasting of scalable video sources. In the proposed system, a transmitter with multiple antennas …