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openai docker 自定义基础容器 服务器配置

基本模式是这样子的.我们需要将服务器上现成的image pull到本地, 按照自己的实验环境进行配置,保存为新的image, 然后再push到服务器上.假设服务器IP为72.13.0.0首先设置安全仓库为服务器Ipvim /etc/docker/daemon.json# 修改内容如下{"insecure-registries":["172.13.0.0"]}# 重启docker 使得配置...

2020-07-31 18:29:17

docker入门课程

Docker 架构Ubuntu Docker 安装使用官方安装脚本自动安装手动安装Docker 镜像加速Docker Hello World运行交互式的容器启动容器(后台模式)停止容器Docker 容器使用获取镜像启动容器启动已停止运行的容器后台运行停止一个容器进入容器导出和导入容器删除容器Docker 镜像使用列出镜像列表获取一个新的镜像查找镜像删除镜像创建镜像更新镜像**构建镜像**设置镜像标签Docker 容器连接Docker 架构Docker 包括三个基本概念:镜像(Image):Dock.

2020-07-30 14:25:04

【DKNN】Distilling the Knowledge in a Neural Network 第一次提出神经网络的知识蒸馏概念

Distilling the Knowledge in a Neural NetworkAbstract1 Introduction2 Distillation2.1 Matching logits is a special case of distillation3 Preliminary experiments on MNIST4 Experiments on speech recognition4.1 Results5 Training ensembles of specialists on ver.

2020-07-23 11:02:22

【DML】Deep Mutual Learning 深度相互学习,神经网络也可以协作

Deep Mutual LearningAbstract1. Introduction2. Related WorkModel DistillationCollaborative Learning3. Deep Mutual Learning3.1. Formulation3.2. Optimisation3.3. Extension to Larger Student Cohorts3.4. Extension to Semi-supervised Learning5. Conclusion论文:h.

2020-07-22 20:09:30

【MMT】ICLR 2020: MMT(Mutual Mean-Teaching)方法,无监督域适应在Person Re-ID上性能再创新高

为了减轻噪音伪标签的影响,文章提出了一种无监督的MMT(Mutual Mean-Teaching)方法,通过在迭代训练的方式中使用离线精炼硬伪标签和在线精炼软伪标签,来学习更佳的目标域中的特征。同时,还提出了可以让Traplet loss支持软标签的soft softmax-triplet loss”。 该方法在域自适应任务方面明显优于所有现有的Person re-ID方法,改进幅度高达18.2%。MUTUAL MEAN-TEACHING: PSEUDO LABEL REFINERY FOR UNSUP.

2020-07-07 19:58:11

【FSR】Feature Space Regularization for Person Re-Identification with One Sample

Feature Space Regularization for Person Re-Identification with One SampleAbstractI. INTRODUCTIONFramework.Our Method.II. RELATED WORKSA. Supervised Re-IDB. Semi-supervised Re-IDC. Unsupervised re-IDD. Progressive LearningIII. THE PROPOSED METHODA. Overall.

2020-07-05 17:36:15

【CRR-FMM】A Concise Review of Recent Few-shot Meta-learning Methods

【CRR-FMM】A Concise Review of Recent Few-shot Meta-learning Methods1 IntroductionMindMap2. The Framework of Few-shot Meta-learning2.1. Notation and definitionsDefinition 1. (Small-sample learning)Definition 2. (Few-shot learning)Definition 3. (Few-shot met.

2020-05-29 19:16:30

少标签数据学习:宾夕法尼亚大学Learning with Few Labeled Data

文章目录Few-shot image classificationThree regimes of image classificationProblem formulationA flavor of current few-shot algorithmsHow well does few-shot learning work today?The key ideaTransductive LearningAn exampleResults on benchmark datasetsThe ImageNet.

2020-05-27 10:42:05

傅立叶的简单入门

参考文献:关于傅立叶的简单入门文章目录傅立叶的简单入门什么是傅立叶级数什么是傅立叶变换频域和时域傅立叶的简单入门将其中的信号表示成一组基本信号的线性组合,便于分析与观察输入和响应的关系来确定系统的特性。第一种分析方法是单位冲激响应的叠加形成的卷积。第二种就是通过傅立叶级数和傅立叶变换。更为深入…什么是傅立叶级数使LTI系统的信号表示成基本信号的组合,这些基本信号必须有两个性质:这些信号能构成相当广泛的一类信号LTI系统对这些信号的响应应该十分简单本身简单,衍生性还强第一幅图是

2020-05-15 09:47:52

卡耐基梅隆大学 Probabilistic Graphical Models 课程 | Elements of Meta-Learning 关于元学习和强化学习

Goals for the lecture:Introduction & overview of the key methods and developments.[Good starting point for you to start reading and understanding papers!]文章目录Probabilistic Graphical Models | Elements of Meta-Learning01 Intro to Meta-LearningMotiv.

2020-05-13 12:21:22

深度学习基础 Probabilistic Graphical Models | Statistical and Algorithmic Foundations of Deep Learning

文章目录Probabilistic Graphical ModelsStatistical and Algorithmic Foundations of Deep Learning01 An overview of DL componentsHistorical remarks: early days of neural networksReverse-mode automatic differentiation (aka backpropagation)Modern building blocks: u.

2020-05-12 18:46:57

最新小样本学习综述 A Survey on Few-Shot Learning | 四大模型Multitask Learning、Embedding Learning、External Memory…

文章目录01 Multitask Learning01.1 Parameter Sharing01.2 Parameter Tying.02 Embedding Learning相关阅读:A Survey on Few-Shot Learning | Introduction and OverviewA Survey of Few-Shot Learing | Data给定少数样本的Dt...

2020-05-11 10:56:59

Generalizing from a Few Examples: A Survey on Few-Shot Learning 小样本学习最新综述 | 三大数据增强方法

文章目录01 Transforming Samples from Dtrain02 Transforming Samples from a Weakly Labeled or Unlabeled Data Set03 Transforming Samples from Similar Data SetsDiscussion and Summary上一篇:A Survey on Few-Shot ...

2020-04-29 15:53:18

pycharm打开项目后只读变为可编辑

情况描述粘贴了一些文件到项目中, 打开pycharm准备编辑,发现文件属于只读模式,点击右下角的解锁按钮也无法编辑.解决方案命令格式:sudo chown -R username Filename输入密码后, 再次打开pycharm项目,便可以编辑了.参考链接: https://blog.csdn.net/weixin_34166472/article/details/863452...

2020-04-24 11:14:29

linux系统能正常上网,但打不开github

问题linux系统能正常上网,但打不开github. 页面空白一直加载不出来内容.解决方法修改 /etc/hosts文件,记得给权限添加内容如下192.30.253.120 codeload.github.com192.30.253.113 github.com192.30.253.113 github.com192.30.253.118 gist.github.com192.3...

2020-04-17 09:56:53

Generalizing from a Few Examples: A Survey on Few-Shot Learning 小样本学习最新综述| Introduction and Overview

Author listYAQING WANG, Hong Kong University of Science and Technology and Baidu ResearchQUANMING YAO∗, 4Paradigm Inc.JAMES T. KWOK, Hong Kong University of Science and TechnologyLIONEL M. NI, Ho...

2020-04-13 22:21:16

Graph Neural Networks图神经网络(一)

Author: Nihai V. Nayak (March 2020)Graph Neural Networks图神经网络01 Introduction02 Basics03 Learning on Graphs03.1 Formal Definition04 Graph Convolutional Networks (GCN)04.1 Aggregate04.2 Combine05 Gr...

2020-04-12 22:32:36

Capsule Networks胶囊网络(二)

文章目录Dynamic RoutingCoefficients operate on capsule levelDynamic Routing: Routing by AgreementComparison to fully connected neural networkfront-up contentComputing input/output vectors of a capsuleRout...

2020-04-12 21:13:06

仅需2步,利用GitHub Page生成网页

针对push到GitHub上面的web项目,利用Github Page生成网页下面举例说明博主已给GitHub page指定域名(joselynzhao.top), 没有域名的朋友,可以直接用{username}.github.io代替。项目目录如下:网站的index页面为/web/cws02.html01 在index file所在目录创建CMAKE文件内容为 Github...

2020-04-11 21:54:35

Capsule Networks胶囊网络(一)

author: Sargur Srihari srihari@buffalo.eduThis is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/CSE676文章目录Limitations of Convolutional NetworksConvolutionalNeuralN...

2020-04-10 23:01:18

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