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Semantic Segmentation using adversarial Networks2018-04-27 09:36:48Abstract:For the production of image modeling, the confrontation training has achieved very good results. In this paper, we propose a method of antagonistic training to train semantic segmentation model. In fact, this is the addition of a confrontation loss, that is: a CNN to determine the given graph is the result of segmentation? Or the GT

WPS Office 2002 is a set of office software suitable for use in China. Many schools use the software, especially in the row of mathematics, physics, Chemistry and other papers, simple and quick formula input is very popular with teachers. But at this time, the production of paper rolls became a problem, many teachers use the method of inserting pictures to make paper volume head, in the actual operation, a

Directsprite classUnlike the Animationsprite class that appeared in the previous document (played from the beginning to the tail)This class is based on the graph of path, if it is 8*8 64 figure eight direction, you can set the width and angleThe angle represents 8 directions.Camera2dangle camera rotation, 2d imitate 3d observation traverse the body.Move2dangle class Eight direction pathfinding animation controlNot finished, to be continuedGameunity 2.

pixels (i,j). In the MLPCONV layer, the rules for each neuron are calculated asn represents the hierarchy of the network. In the above-mentioned B-diagram, for each neuron, the generated only a single output, and the input is multidimensional (can be understood as multi-channel, each layer in the network is a 1*k vector), the entire process can be regarded as a 1*1*k convolution layer on the K-channel. In some subsequent papers, this method is commonly used to reduce the dimension of the input

Hybrid computing using a neural network with dynamic external memoryNature 2016Original link:http://www.nature.com/nature/journal/vaop/ncurrent/pdf/nature20101.pdf　　absrtact : AI Neural Networks have been very successful in perceptual processing, sequence learning, reinforcement learning, but limited to their ability to represent variables and data structures, and the ability to store knowledge for long periods of time because of the lack of an additional memory unit. Here, we introduce a machin

article Everyone's mind is fragile, no matter who it is. In the same process of using soft text promotion, if you want users to browse your soft text, there must be a stimulating point in the user's mind, so that the user's mind will feel refreshed, so that the user can carefully browse your soft paper. Adding emotional colors to soft texts is naturally the easiest and most direct way to penetrate the user's mind. For example, the current TV series a

One: Important principles(1) Chain rules:(2) Bayesian theorem:(3) Condition independence between variables:Two: Major issuesProbabilistic inference of 2.1 Bayesian networks2.2 Structural Learning: Discovering graph relationships between variablesStructure Learning algorithm:(1) K2 algorithm: Learn Bayesian network structure by finding the parent node set for each node. It continually adds nodes to the parent node and selects a set of nodes that maximi

,y) specifically draws the string The Add (component) method exists in the container class, which uses this method to add components to the frame through the component name JAVA2D Library, the parametric graphics in paintcomponent, as long as this parameter is coerced into a graphics2d type and passed to this type of variable reference, to draw a graph, The first thing to do is to create an object that implements the shape interface's class (Graph

The citation for this paper is not high, but a very interesting point of view is to unify the whole link layer with the convolution layer. Many of the following classic network structures, including GOOGLENET,FCN, should be inspired by them. The author is Yinhui into a team, Caffe Model Zoo also see NIN figure, or very influential.Technical SummaryImproved the structure of traditional CNN. It is said that each convolution layer is replaced by a small

Read a period of time goal detection of the paper, here to write a summary of the article. Do not necessarily understand correctly, if there are problems, please correct me. 1, RCNN RCNN is based on selective search (SS) Searching region proposal (RP), and then on each RP CNN inference, this algorithm is more direct.The framework should also be easy to see and understand.SS extracts approximately 2K of RP for each picture, and then infers the RP. 2, S

behavior: The paper cannot be fully placed in the tray. Reason: paper disk size adjustment is not correct. Workaround: Paper Tray frame diagram: 1. Pull out the paper tray. 2. Before loading the paper, bend or fan the pap