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Network学习代码

网络接口的封装,方便编程和学习,一种不错的思路供参考。
2018-04-15 上传大小:39KB
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几篇CVPR关于multi-task的整理

几篇CVPR关于multi-task的论文笔记整理,包括 一、 多任务课程学习Curriculum Learning of Multiple Tasks 1 --------------^CVPR2015/CVPR2016v--------------- 5 二、 词典对分类器驱动卷积神经网络进行对象检测Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection 5 三、 用于同时检测和分割的多尺度贴片聚合(MPA)* Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation ∗ 7 四、 通过多任务网络级联实现感知语义分割Instance-aware Semantic Segmentation via Multi-task Network Cascades 10 五、 十字绣网络多任务学习Cross-stitch Networks for Multi-task Learning 15 --------------^CVPR2016/CVPR2017v--------------- 23 六、 多任务相关粒子滤波器用于鲁棒物体跟踪Multi-Task Correlation Particle Filter for Robust Object Tracking 23 七、 多任务网络中的全自适应特征共享与人物属性分类中的应用Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification 28 八、 超越triplet loss:一个深层次的四重网络,用于人员重新识别Beyond triplet loss: a deep quadruplet network for person re-identification 33 九、 弱监督级联卷积网络Weakly Supervised Cascaded Convolutional Networks 38 十、 从单一图像深度联合雨水检测和去除Deep Joint Rain Detection and Removal from a Single Image 43 十一、 什么可以帮助行人检测?What Can Help Pedestrian Detection? (将额外的特征聚合到基于CNN的行人检测框架) 46 十二、 人员搜索的联合检测和识别特征学习Joint Detection and Identification Feature Learning for Person Search 50 十三、 UberNet:使用多种数据集和有限内存训练用于低,中,高级视觉的通用卷积神经网络UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory 62 一共13篇,希望能够帮助到大家

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siamese mnist 孪生网络例子

在mnist数据集上简单实现了孪生网络的tensorflow代码,包括训练过程,测试过程和图示过程。代码包中自带图像,可以直接运行。

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pointer network 用到的数据

https://github.com/devsisters/pointer-network-tensorflow 的 tsp_10_train.zip

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the ONE无线网络模拟器(Opportunistic Network Environment simulator )

芬兰人开发的一个专门用于DTN网络仿真的软件,用Java语言写的,该模拟器本身能支持First Contact, Epidemic, Spray and Wait, Direct delivery, PRoPHET and MaxProp这几种路由。 具体的安装方法里面有一个README.txt,很有用,不仅介绍了the ONE的一些性能,还有具体的安装以及示例。由于本身只是一些源代码,所以需要用户重新编译。具体的方法是: 1、安装Java5,配置好路径等; 2、点击文件夹下的compile.bat进行编译; 3、运行one.bat,就可以看见GUI了,此时模拟的是default。txt配置的DTN网络,如果要自己配置的DTN网络,则输入one My*.txt即可。 希望能对大家有用,另外还有一个用于DTN网络模拟的软件叫DTNsim2,是加拿大的waterloo 大学开发的

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Bayesian Network(贝叶斯网络) Python Program

用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets in an XML format. SampleNets.py: Some code that constructs a sample Bayes net. This is called when SIGMAEditor.py is started up. SIGMAEditor.py: A main program that can be turned into an experimental application by adding menus, more code, etc. It has some facilities already for loading event sequence files and playing them. sample-event-file.txt: A sequence of events that exemplifies the format for these events. gma-mona.igm: A sample Bayes net in the form of an XML file. The SIGMAEditor program can read this type of file. Here are some limitations of the toolkit as of 23 February 2009: 1. Users cannot yet edit Bayes nets directly in the SIGMAEditor. Code has to be written to create new Bayes nets, at this time. 2. If you select the File menu's option to load a new Bayes net file, you get a fixed example: gma-mona.igm. This should be changed in the future to bring up a file dialog box so that the user can select the file. 3. When you "run" an event sequence in the SIGMAEditor, the program will present each event to each input node and find out if the input node's filter matches the evidence. If it does match, that fact is printed to standard output, but nothing else is done. What should then happen is that the node's probability is updated according to its response method, and if the new probability exceeds the node's threshold, then its successor ("children") get their probabilities updated, too. 4. No animation of the Bayes net is performed when an event sequence is run. Ideally, the diagram would be updated dynamically to show the activity, especially when posterior probabilities of nodes change and thresholds are exceeded. To use the BNT, do three kinds of development: A. create your own Bayes net whose input nodes correspond to pieces of evidence that might be presented and that might be relevant to drawing inferences about what's going on in the situation or process that you are analyzing. You do this by writing Python code that calls constructors etc. See the example in SampleNets.py. B. create a sample event stream that represents a plausible sequence of events that your system should be able to analyze. Put this in a file in the same format as used in sample-event-sequence.txt. C. modify the code of BNT or add new modules as necessary to obtain the functionality you want in your system. This could include code to perform actions whenever an output node's threshold is exceeded. It could include code to generate events (rather than read them from a file). And it could include code to describe more clearly what is going on whenever a node's probability is updated (e.g., what the significance of the update is -- more certainty about something, an indication that the weight of evidence is becoming strong, etc.)

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AUTOSAR_SWS_CANNetworkManagement

官方规范 4.2.0版本 This document describes the concept, core functionality, configurable features, interfaces and configuration issues of the AUTOSAR CAN Network Management (CanNm). The AUTOSAR CAN Network Management is a hardware independent protocol that can only be used on CAN (for limitations refer to chapter 4.1). Its main purpose is to coordinate the transition between normal operation and bus-sleep mode of the network.

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A C-LSTM Neural Network for Text Classification.pdf

一篇论文,结合了cnn和lstm的深度网络用来做文本分类。

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R-CNN matlab 代码: Regions with Convolutional Neural Network Features

R-CNN, matlab 代码: Regions with Convolutional Neural Network Features,卷积神经网络,此代码容易看懂,实现,适合学习 改进!

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CapsuleNetWork:从TensorFlow复现代码理解胶囊网络(DynamicRoutingBetweenCapsules)

从TensorFlow复现代码理解胶囊网络(Dynamic Routing Between Capsules) 论文链接:https://arxiv.org/abs/1710.09829 Tensorflow代码复现链接:https://github.com/naturomics/CapsNet-Tensorflow

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network学习文档

如果有需要.希望能帮上忙.关于网络学习方面的

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Capsule胶囊网络官方版本-github_Sara-Tensorflow_python代码

Capsule胶囊网络官方版本,来自作者Sara的Github。只下载了capsule一部分,方便不能fanqiang和不想clone整个project的。原资源:https://github.com/Sarasra/models/tree/master/research/capsules

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PhotonUnityNetwork(PUN)

最新unity网络游戏开发插件,本人亲测有效,unity多人网络游戏开发必备插件! 最新unity网络游戏开发插件,本人亲测有效,unity多人网络游戏开发必备插件!

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Siamese网络训练和预测guide

唯一的一份在windows上进行Siamese网络训练和预测的指导 帮助你少走弯路

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Cooper_Learning_Bayesnets

Bayes Network学习和建模介绍

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吴恩达深度学习Logistic Regression with a Neural Network mindset

本人把网页版的转换为电脑版python工程了,加了俩个测试函数。

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回声状态网络法echo state network代码(简单)

esn作者在官网的简单版代码,给英文不好的同学拿过来,免得再去找地方下载

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fcn(fully convolutional network)源代码

fcn源代码,可用siftflow、voc、nyud、pascalcontext等数据集

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Boost.Asio C++ Network Programming Cookbook

Boost.Asio C++ Network Programming Cookbook 英文原版+代码

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Social Network Analysis for Startups [2011]

Social Network Analysis for Startups [2011]代码

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[随书代码]Codes for《Neural Network Programming with Java》

《Neural Network Programming with Java》随书代码

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Network学习代码

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