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<html lang="zh"><head><meta charset="utf-8"><meta name="generator" content="Hexo 4.2.0"><meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1"><meta><title>Digital Shiyu</title><meta description="Trying to be everything about me"><meta property="og:type" content="blog"><meta property="og:title" content="Digital Shiyu"><meta property="og:url" content="http://yoursite.com/"><meta property="og:site_name" content="Digital Shiyu"><meta property="og:description" content="Trying to be everything about me"><meta property="og:locale" content="zh_CN"><meta property="og:image" content="http://yoursite.com/img/og_image.png"><meta property="article:author" content="shiyu AllRightsReserved"><meta property="article:tag" content="work"><meta property="article:tag" content=" life"><meta property="article:tag" content=" thoughts"><meta property="twitter:card" content="summary"><meta property="twitter:image" content="/img/og_image.png"><script type="application/ld+json">{"@context":"https://schema.org","@type":"BlogPosting","mainEntityOfPage":{"@type":"WebPage","@id":"http://yoursite.com"},"headline":"Digital Shiyu","image":["http://yoursite.com/img/og_image.png"],"author":{"@type":"Person","name":"shiyu AllRightsReserved"},"description":"Trying to be everything about me"}</script><link rel="icon" href="/img/digitalpre.png"><link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.12.0/css/all.css"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/highlight.js@9.12.0/styles/atom-one-light.css"><link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Ubuntu:wght@400;600&family=Source+Code+Pro"><link rel="stylesheet" href="/css/default.css"><!--!--><!--!--><script src="//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js" defer></script><!--!--><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/lightgallery@1.6.8/dist/css/lightgallery.min.css"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/justifiedGallery@3.7.0/dist/css/justifiedGallery.min.css"><script src="https://www.googletagmanager.com/gtag/js?id=UA-165282104-1" async></script><script>window.dataLayer = window.dataLayer || [];
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gtag('config', 'UA-165282104-1');</script><!--!--><!--!--><script src="https://cdn.jsdelivr.net/npm/pace-js@1.0.2/pace.min.js"></script></head><body class="is-2-column"><nav class="navbar navbar-main"><div class="container"><div class="navbar-brand justify-content-center"><a class="navbar-item navbar-logo" href="/"><img src="/img/Digital.png" alt="Digital Shiyu" height="28"></a></div><div class="navbar-menu"><div class="navbar-start"><a class="navbar-item is-active" href="/">主页</a><a class="navbar-item" href="/archives">存档</a><a class="navbar-item" href="/categories">分类</a><a class="navbar-item" href="/tags">标签</a><a class="navbar-item" href="/about">关于</a></div><div class="navbar-end"><a class="navbar-item search" title="搜索" href="javascript:;"><i class="fas fa-search"></i></a></div></div></div></nav><section class="section"><div class="container"><div class="columns"><div class="column order-2 column-main is-8-tablet is-8-desktop is-8-widescreen"><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/27/Music/%E4%BA%94%E7%BA%BF%E8%B0%B1/"><img class="thumbnail" src="/gallery/test.png" alt="五线谱"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-27T06:31:29.000Z" title="2020-09-27T06:31:29.000Z">2020-09-27</time><span class="level-item"><a class="link-muted" href="/categories/%E9%9F%B3%E4%B9%90/">音乐</a></span><span class="level-item">3 分钟 读完 (大约 482 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/27/Music/%E4%BA%94%E7%BA%BF%E8%B0%B1/">五线谱</a></h1><div class="content"><p>根据 <a href="https://www.youtube.com/watch?v=R_4MThtf49E">doyoudo</a>的教程整理的笔记</p>
<h2 id="第一节：识谱"><a href="#第一节：识谱" class="headerlink" title="第一节：识谱"></a>第一节：识谱</h2><ol>
<li><strong>乐符</strong>：五线谱中的基本组成元素为乐符，其中符号由下往上分为符头，符干和符尾，这三者用来区分不同的音符时值，只有一个空心符头则称为全音符，下面是各种不同时值音符的表示方式<br><img src="/Pics/staff1.png" alt="乐符"></div><a class="article-more button is-small size-small" href="/2020/09/27/Music/%E4%BA%94%E7%BA%BF%E8%B0%B1/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/27/Music/%E4%B9%90%E7%90%86/"><img class="thumbnail" src="/gallery/beat.png" alt="乐理知识"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-27T01:27:56.000Z" title="2020-09-27T01:27:56.000Z">2020-09-27</time><span class="level-item"><a class="link-muted" href="/categories/%E9%9F%B3%E4%B9%90/">音乐</a></span><span class="level-item">12 分钟 读完 (大约 1830 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/27/Music/%E4%B9%90%E7%90%86/">乐理知识</a></h1><div class="content"><p>根据 <a href="https://www.youtube.com/watch?v=H8564udAw7c&list=PLt6xeHPISLq6DlI7LkEnq9kzu9YGik8s4">doyoudo</a> 的教程整理的笔记</p>
<h2 id="第一节：节拍"><a href="#第一节：节拍" class="headerlink" title="第一节：节拍"></a>第一节：节拍</h2><ol>
<li>拍：描述音乐节奏的基本单位，一拍就是一次击打</li>
<li>速度：beat per minute 每分钟有多少拍</li>
<li>小节: 根据一定数量的拍就能确定一个小节</li>
<li>音符时值：每个音符占据一个小节的长度，占据全部长度的是全音符，占据1/2长度的是半音符，其他四分音符，八分音符则以此类推</li>
<li>4/4 节拍描述：分子表示，每小节拍的数量（四拍），分母表示每一拍的音符时值——以四分音符为一拍（为什么感觉上下描述的是一个意思/捂脸）<br><img src="/Pics/beat44.png" alt="4/4拍"></div><a class="article-more button is-small size-small" href="/2020/09/27/Music/%E4%B9%90%E7%90%86/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/16/CPP/%E4%BD%BF%E7%94%A8%E5%AD%97%E7%AC%A6%E4%B8%B2/"><img class="thumbnail" src="/gallery/cpp.png" alt="使用字符串"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-16T02:37:31.000Z" title="2020-09-16T02:37:31.000Z">2020-09-16</time><span class="level-item"><a class="link-muted" href="/categories/cpp/">cpp</a></span><span class="level-item">7 分钟 读完 (大约 1084 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/16/CPP/%E4%BD%BF%E7%94%A8%E5%AD%97%E7%AC%A6%E4%B8%B2/">使用字符串</a></h1><div class="content"><p>这一节，利用字符串读取和输出的程序，了解字符串的相关操作，常量，和变量的声明以及初始化等概念。</p>
<p>学习要点：</p>
<ol>
<li>变量和对象的区分</li>
<li>链式输入和输出缓存区</li>
<li>字符类型的操作，运算符重载等</li>
</ol></div><a class="article-more button is-small size-small" href="/2020/09/16/CPP/%E4%BD%BF%E7%94%A8%E5%AD%97%E7%AC%A6%E4%B8%B2/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/BDL/"><img class="thumbnail" src="/gallery/BDL_TEASOR.png" alt="Bidirectional Learning for Domain Adaptation of Semantic Segmentation"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-15T07:27:42.000Z" title="2020-09-15T07:27:42.000Z">2020-09-15</time><span class="level-item"><a class="link-muted" href="/categories/%E9%A2%86%E5%9F%9F%E8%87%AA%E9%80%82%E5%BA%94/">领域自适应</a></span><span class="level-item">6 分钟 读完 (大约 909 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/BDL/">Bidirectional Learning for Domain Adaptation of Semantic Segmentation</a></h1><div class="content"><h2 id="主要思想"><a href="#主要思想" class="headerlink" title="主要思想"></a>主要思想</h2><p>在图像变换和分割网络中融入perception loss 来减少不同特征对分割网络的影响，随后使用双向学习和自监督学习提升网络的泛化能力，并使得两阶段网络不断互相促进。<br>其中双向学习使得图像变换网络和分割网络不断迭代更新，相互促进优化，自监督学习使用分类器输出结果给目标域图片分配伪标签来约束分割网络</p>
<p><img src="/Pics/BDL_net.png" alt="网络结构"><br></div><a class="article-more button is-small size-small" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/BDL/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CAG_UDA/"><img class="thumbnail" src="/gallery/CAG_TEASOR.png" alt="CAG_UDA"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-15T01:34:11.000Z" title="2020-09-15T01:34:11.000Z">2020-09-15</time><span class="level-item"><a class="link-muted" href="/categories/%E9%A2%86%E5%9F%9F%E8%87%AA%E9%80%82%E5%BA%94/">领域自适应</a></span><span class="level-item">10 分钟 读完 (大约 1565 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CAG_UDA/">CAG_UDA</a></h1><div class="content"><h2 id="Category-Anchor-Guided-Unsupervised-Domain-Adaptation-for-Semantic-Segmentation"><a href="#Category-Anchor-Guided-Unsupervised-Domain-Adaptation-for-Semantic-Segmentation" class="headerlink" title="Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation"></a>Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation</h2><h3 id="中心思想："><a href="#中心思想：" class="headerlink" title="中心思想："></a>中心思想：</h3><p>核心思想为基于源域类别 Anchor 的分布对齐，实现两个域之间类内距离减小，类间距离增大的目的，更加利于生成分界面，同时使用对目标与分配伪标签的方式促使分界面不从数据中心穿过，也减少分类器对源域的偏爱。</p>
<ol>
<li>类别层次的特征对齐：基于源域和目标域相同类别的特征向量在特征空间中距离较近的假设，把源域的每个类别上计算类别的平均值当成是类别中心，并促使源域的同一类别特征向量和目标域的激活特征向量向类别中心靠拢。</li>
<li>提升模型泛化能力：基于源域 Anchor 给激活的目标域特征分配伪标签，分类器使用伪标签进行训练，促使分类边界也根据目标域的标签进行相应的调整。</div><a class="article-more button is-small size-small" href="/2020/09/15/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CAG_UDA/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/12/CPP/%E5%BC%80%E5%A7%8B%E5%AD%A6%E4%B9%A0cpp/"><img class="thumbnail" src="/gallery/cpp.png" alt="开始学习C++"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-12T08:15:01.000Z" title="2020-09-12T08:15:01.000Z">2020-09-12</time><span class="level-item"><a class="link-muted" href="/categories/cpp/">cpp</a></span><span class="level-item">5 分钟 读完 (大约 761 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/12/CPP/%E5%BC%80%E5%A7%8B%E5%AD%A6%E4%B9%A0cpp/">开始学习C++</a></h1><div class="content"><p>本章开始，通过阅读《Accelerated C++》开始记录笔记并学习<br>学习要点：</p>
<ol>
<li>标准库和其代表的名字空间</li>
<li>表达式：被操作数和运算符组成了一个表达式，其中运算符有左结合/右结合的性质，被操作数则是由其类型决定表达式的结果</li>
<li>作用域：学习了两种作用域的生成方式，分别是花括号和名字空间</li>
</ol>
<figure class="highlight c++"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">#<span class="meta-keyword">include</span> <span class="meta-string"><iostream></span></span></span><br><span class="line"></span><br><span class="line"><span class="comment">//这是一个简单的cpp程序</span></span><br><span class="line"><span class="function"><span class="keyword">int</span> <span class="title">main</span><span class="params">()</span></span></span><br><span class="line"><span class="function"></span>{</span><br><span class="line"> <span class="built_in">std</span>::<span class="built_in">cout</span> << <span class="string">"Hello, World!"</span> <<<span class="built_in">std</span>::<span class="built_in">endl</span>;</span><br><span class="line"> <span class="keyword">return</span> <span class="number">0</span>;</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<p>在这个简单的程序中，我们将学习到表达式，作用域，运算符，作用数等一系列的概念</p>
<h2 id="1-注释："><a href="#1-注释：" class="headerlink" title="1. 注释："></a>1. 注释：</h2><p>可以使用 // 进行单行注释，也可以使用/<em>*/ 来进行多行注释（每次跨行需要行首加上 </em> ），当使用// 时，其优先级会高于多行注释</p>
<h2 id="2-include："><a href="#2-include：" class="headerlink" title="2. include："></a>2. include：</h2><p>使用 include 语句来包含不属于<strong>语言核心</strong>的<strong>标准库</strong>来增加对额外的指出<br></div><a class="article-more button is-small size-small" href="/2020/09/12/CPP/%E5%BC%80%E5%A7%8B%E5%AD%A6%E4%B9%A0cpp/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/"><img class="thumbnail" src="/gallery/senet.png" alt="注意力机制"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-09T09:29:55.000Z" title="2020-09-09T09:29:55.000Z">2020-09-09</time><span class="level-item"><a class="link-muted" href="/categories/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span><span class="level-item">6 分钟 读完 (大约 877 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/">注意力机制</a></h1><div class="content"><h2 id="1-简介"><a href="#1-简介" class="headerlink" title="1. 简介"></a>1. 简介</h2><h2 id="2-SEnet-Squeeze-excitation-network"><a href="#2-SEnet-Squeeze-excitation-network" class="headerlink" title="2. SEnet (Squeeze-excitation network)"></a>2. SEnet (Squeeze-excitation network)</h2><h2 id="3-SKNET-CBAM-等"><a href="#3-SKNET-CBAM-等" class="headerlink" title="3. SKNET, CBAM 等"></a>3. SKNET, CBAM 等</h2><h2 id="1-简介-1"><a href="#1-简介-1" class="headerlink" title="1. 简介"></a>1. 简介</h2><p>定义：通过一定方式使得学习过程中仅仅关注部分信息的的手段都可以称作注意力机制，其可以让网络仅仅关注某些有用的信息，获取了关键信息就可以使用更加少的参数获得更加好的效果。</p></div><a class="article-more button is-small size-small" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/#more">阅读更多</a></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/09/%E6%89%80%E8%A7%81%E6%89%80%E9%97%BB/post/"><img class="thumbnail" src="/gallery/test.png" alt="见闻"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-09T08:33:24.000Z" title="2020-09-09T08:33:24.000Z">2020-09-09</time><span class="level-item"><a class="link-muted" href="/categories/%E8%A7%81%E9%97%BB/">见闻</a></span><span class="level-item">几秒 读完 (大约 0 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/09/%E6%89%80%E8%A7%81%E6%89%80%E9%97%BB/post/">见闻</a></h1><div class="content"></div></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/09/%E6%97%A5%E6%9C%89%E6%89%80%E6%80%9D/post/"><img class="thumbnail" src="/gallery/test.png" alt="思考"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-09T08:32:55.000Z" title="2020-09-09T08:32:55.000Z">2020-09-09</time><span class="level-item"><a class="link-muted" href="/categories/%E6%80%9D%E8%80%83/">思考</a></span><span class="level-item">几秒 读完 (大约 0 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/09/%E6%97%A5%E6%9C%89%E6%89%80%E6%80%9D/post/">思考</a></h1><div class="content"></div></article></div><div class="card"><div class="card-image"><a class="image is-7by3" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/"><img class="thumbnail" src="/gallery/LinearR.png" alt="线性回归"></a></div><article class="card-content article" role="article"><div class="article-meta size-small is-uppercase level is-mobile"><div class="level-left"><time class="level-item" dateTime="2020-09-09T07:56:10.000Z" title="2020-09-09T07:56:10.000Z">2020-09-09</time><span class="level-item"><a class="link-muted" href="/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a></span><span class="level-item">8 分钟 读完 (大约 1152 个字)</span></div></div><h1 class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/">线性回归</a></h1><div class="content"><h1 id="线性回归（基于神经网络-梯度下降）"><a href="#线性回归（基于神经网络-梯度下降）" class="headerlink" title="线性回归（基于神经网络+梯度下降）"></a>线性回归（基于神经网络+梯度下降）</h1><p><strong>定义</strong>：基于特征和标签之间的线性函数关系约束，线性回归通过建立单层神经网络，将神经网络中每一个神经元当成是函数关系中的一个参数，通过利用初始输出和目标输出建立损失，并优化损失最小的方式使得神经元的数值和真实函数参数数值最相近，从而通过网络训练得到最符合数据分布的函数关系。</p>
<p><strong>实施步骤</strong>：</p>
<ol>
<li>初始通过随机化线性函数的参数，通过输入的x，会得到一系列y_h</li>
<li>输出的y_h和真实值y之间因为神经元参数不正确产生差距，为了y_h和y能尽量地逼近，我们通过平方误差损失函数（MSE Loss）来描述这种误差。</li>
<li>类似于通过求导得到损失函数最优解的方式，我们通过梯度下降法将这种误差传递到参数，通过调整参数使误差达到最小</li>
<li>通过几轮的训练，我们得到的最小的损失值对应的神经元数值，就是描述输入输出的线性关系的最好的参数。</li>
</ol></div><a class="article-more button is-small size-small" href="/2020/09/09/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/#more">阅读更多</a></article></div><nav class="pagination is-centered mt-4" role="navigation" aria-label="pagination"><div class="pagination-previous is-invisible is-hidden-mobile"><a href="/page/0/">上一页</a></div><div class="pagination-next"><a href="/page/2/">下一页</a></div><ul class="pagination-list is-hidden-mobile"><li><a class="pagination-link is-current" href="/">1</a></li><li><a class="pagination-link" href="/page/2/">2</a></li></ul></nav></div><div class="column column-left is-4-tablet is-4-desktop is-4-widescreen order-1"><div class="card widget"><div class="card-content"><nav class="level"><div class="level-item has-text-centered flex-shrink-1"><div><figure class="image is-128x128 mx-auto mb-2"><img class="is-rounded" src="/img/avatar_new.jpg" alt="Daisy"></figure><p class="title is-size-4 is-block line-height-inherit">Daisy</p><p 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