In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic...
1. Learning Dual Convolutional Neural Networks for Low-Level Vision Low-level vision tasks usually involve the estimation of two components, low-frequency structures and high-frequency details 低层视觉问题通常涉及到目标结果的结构和细节部分的估计, 这个Dual Conv包含了两个分支, 分别可以端...
We propose a novel dual-domain convolutional neural network framework to improve structural information of routine 3 T images. We introduce a parameter-efficient butterfly network that involves two complementary domains: a spatial domain and a frequency domain. The butterfly network allows the intera...
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...
卷积神经网络 CNN 文章目录卷积神经网络 CNN一、概述二、卷积的概念三、CNN原理3.1 卷积层3.2 池化层3.3 完全连接层3.4 权值矩阵BP算法3.5 层的尺寸设置四、CNN简单使用五、总结 一、概述 卷积神经网络(Convolutional Neural Network, CNN)是一种前馈神经网络,它的人工神经元可以响应一部分覆盖范围内的周围单元...
This paper presents a new dual-channel convolutional neural network (CNN) which is designed to SAR image change detection to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN structures, which can extract features from two multitemporal SAR...
In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DST...
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 (ConvNets) based methods can learn both the warping and blending tasks jointly. Such methods are often designed for moderate ... N Hobloss,L Zhang,S Lathuiliere,... - 《Signal Processing Image Communication A Publication of the the European Association for Signal ...
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...