capsule networks

With the popularization of Wireless terminals, more and more devices need to use wireless networks. For enterprises, deploying wireless networks is imminent. Now wireless Internet access has become a popular trend. for small and medium-sized enterprises, due to its ease of use and low investment, wireless networks have gradually become a new trend in the Internet

Original sourceThank the Author ~Faster r-cnn:towards Real-time Object Detection with region Proposalnetworksshaoqing Ren, kaiming He, Ross girshick, Jian SuNSummaryAt present, the most advanced target detection network needs to use the region proposed algorithm to speculate on the target location, such as sppnet[7] and fast r-cnn[5] These networks have reduced the running time of the detection network, then the calculation of the region is a bottlene

in multiple adapters or interfacesEthtool-s Displaying network statisticsEthtool Speed netstatDiscover the most useful and common Linux commands for host connections. You can use "netstat-g" to query all multicast groups (networks) that the host subscribes toNetstat-nap | grep Port will show the process ID of the application using that portNetstat-a or Netstat–all will display all connections that include TCP and UDPNetstat–tcp or netstat–t will disp

3D Graph Neural Networks for RGBD Semantic segmentation2018-04-13 19:19:481. Introduction:With the development of depth sensors, RGBD semantic segmentation is applied to many problems, such as virtual reality, robot, human-computer interaction and so on. Compared with the existing 2D semantic segmentation, RGBD semantic segmentation can use real-world geometric information to assist segmentation by exploring depth information. As shown in the normal 2

Basic informationSection Two, Water divisionAutomatic water-body segmentation from high-resolution satellite Images via deep NetworksNotes starting point Water Division is the basic task of remote sensing. The traditional method relies on spectra and can only handle low resolution images. And the resolution of the picture, contains more details. The robustness of the method is tested by the data obtained from different data sensors. The main innovation point Propos

! Each function you'll implement'll have detailed instructions that'll walk you through the steps needed:convolution Functions, Including:zero Padding convolve window convolution forward convolution backward (optional) pooling functions, Including:pooling forward Create Mask distribute value pooling backward (optional) This notebook would ask you for implement these functions from scratch in numpy. In the next notebook, you'll use the TensorFlow equivalents of this functions to build the followi

follows: The first step is to perform feature map selection for the conv4-3 and conv5-3 layers of the Vgg network for a given target, which is to select the most relevant feature maps, the specific reason being to construct a regular objective function of the L1 norm. In the second step, based on the feature maps of Conv5-3, a universal network gnet is constructed to capture the category information of the target. The third step, based on the conv4-3 feature maps, constructs a specific network

. The artificial intelligence technology in game programming (serial one) Introducing neural networks in normal language(Neural Networks in Plain 中文版) Because we do not have a good understanding of the brain, we often try to use the latest technology as a model to explain it. When I was a child, we all believed that the brain was a telephone switch. (Otherwise, what else could it be?) I also saw

Application examples of RNN--a language model based on RNN Now, let's introduce a model based on the RNN language. We first input the word into the recurrent neural network, each input word, the recurrent neural network output so far, the next most likely word. For example, when we enter in turn: I was late for school yesterday. The output of the neural network is shown in the following figure: where S and e are two special words that represent the beginning and the end of a sequence, resp

machine translation) system, which uses current state-of-the-art training techniques to achieve the greatest increase in machine translation quality so far. For details of all our findings, please refer to our paper "Google's neural machine translation system:bridging the Gap between Human and machine translation" (see end) [1]. A few years ago, we started using recurrent neural networks (rnn:recurrent neural Ne