Dual convolutional neural networkIMAGEREMOVALWe propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining, and dehazing. These problems usually involve estimating two components of the target signals: ...
Convolutional Neural Networks (CNN)Multi-task learningStudent performance prediction is of great importance to many educational domains, such as academic early warning and personalized teaching, and has drawn numerous research attention in recent decades. Most of the previous studies are based on ...
We model a function gθ (f,m) = u using a convolutional neural network (CNN), where θ are network parameters. The registration field \(\phi\) is stored in a n + 1-dimensional image. In other words, for each \(p \in \Omega\), \(u\left( p \right)\) is a ...
Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. Lots of efforts have been made to deepen the CNNs and extract deeper features, while relatively few works pay attention to the receptive field of the network. In this paper...
Graph Neural Networks [16] have achieved remarkable performance at capturing the message diffusion between users and items. As a typical work, NGCF [17] uses Graph Convolutional Network (GCN) [18] to simulate user preference diffusion in a user–item interaction graph. However, most of these ...
之前给介绍了牛逼的CNN(Convolutional Neural Network)和深度学习加速神器BNN(Binarized neural network)。今天给大家介绍深度学习模型中最淘气的模型:在游戏中学习的对偶学习(dual learning)。对偶学习之发表在去年的NIPS(Neural Information Processing Systems)的论文《Neural Information Processing Systems》提出来的一种基...
A Dual Path Network (DPN) is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that ResNets enables feature re-usage while DenseNet enables new feature exploration, and both are important for learning good representations. To enjoy the ben...
To solve the above problems, Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detection (abbreviated as DC-CNN) is proposed. This model benefits from Skip-Gram and Fasttext. It can effectively reduce noisy data and improve the learning ability of the model for non-...
Convolutional neural networks (CNNs) are widely used in computer vision because of their great success in target recognition and classification. However, CNNs are not perfect, and their ability to deal with the spatial relationships of image entities is inadequate. Routing refers to the process tha...
Dual-input regression neural network Data stream Vector diagram 1 Introduction Due to the flexibility, survivability and long-distance transmission, shortwave communication has always been a reserved and development method in the field of wireless communication. Shortwave signal automatic recognition technology...