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cv_content.html
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<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- 上述3个meta标签*必须*放在最前面,任何其他内容都*必须*跟随其后! -->
<title>Computer Vision</title>
<!-- Bootstrap core CSS -->
<link href="bootstrap/css/bootstrap.min.css" rel="stylesheet">
<link href="bootstrap/css/bootstrap-theme.min.css" rel="stylesheet">
<!-- Bootstrap core js -->
<script src="jquery-3.4.1.min.js"></script>
<script src="bootstrap/js/bootstrap.min.js"></script>
</head>
<body>
<nav class="navbar navbar-expand-sm bg-dark">
<a class="navbar-brand text-light" href="index.html">  网站主页</a>
</nav>
<br>
<div class="container">
<div class="card">
<div class="card-head">
<h1>计算机视觉笔记</h1>
</div>
<div class="card-body">
<p>
这里包含计算机视觉各方向的经典论文的原创笔记以及一些源代码分析与复现关键步骤。
<ul>
<li>基础知识与Pytorch源码:Pytorch是目前最火的python优先深度学习库,其代码简洁优雅。
这部分包含一些源代码分析与深度学习基础概念的解释,包括损失函数,优化器,初始化,IOU等概念。</li>
<li>生成对抗模型:生成符合以有数据分布的新数据。例如:生成假的人像,将马变成斑马,生成二次元风格自拍照等。</li>
<li>图像分类模型:顾名思义,用于图像分类。分类模型同时还具有提取图片深层次语义信息的强大功能,是一切任务的基础。</li>
<li>目标检测模型:检测一张数字图片中出现物体的位置与类别信息。</li>
</ul>
</p>
</div>
</div>
<div id="accordion">
<div class="card">
<div class="card-header">
<a class="card-link" data-toggle="collapse" href="#collapse1">基础知识与Pytorch源码</a>
</div>
<div id="collapse1" class="collapse" data-parent="#accordion">
<ul class="list-group">
<li class="list-group-item"><a href="cv_blog/0002_交叉熵.html">交叉熵</a></li>
<li class="list-group-item"><a href="cv_blog/0008_Pytorch操作查询.html">Pytorch基础操作</a></li>
<li class="list-group-item"><a href="cv_blog/0011_GAN训练技巧.html">训练GAN的10个技巧</a></li>
<li class="list-group-item"><a href="cv_blog/0016_生成图像质量评估.html">GAN生成图像质量评估方法</a></li>
<li class="list-group-item"><a href="cv_blog/0017_GAN的损失函数.html">GAN的损失函数总结</a></li>
<li class="list-group-item"><a href="cv_blog/0018_感知机损失.html">感知机损失</a></li>
</ul>
</div>
<div class="card-header">
<a class="card-link" data-toggle="collapse" href="#collapse2">生成对抗模型</a>
</div>
<div id="collapse2" class="collapse" data-parent="#accordion">
<ul class="list-group">
<li class="list-group-item"><a href="cv_blog/0001_GAN.html">NIPS14-GAN:最初的生成对抗模型</a></li>
<li class="list-group-item"><a href="cv_blog/0003_CycleGAN.html">ICCV17-CycleGAN:加入循环一致性的GAN</a></li>
<li class="list-group-item"><a href="cv_blog/0004_StarGAN.html">CVPR18-StarGAN:CycleGAN扩展到多域迁移</a></li>
<li class="list-group-item"><a href="cv_blog/0005_UGATIT.html">ICLR20-UGATIT:使用注意力机制生成二次元头像</a></li>
<li class="list-group-item"><a href="cv_blog/0006_AttentionGAN.html">arXiv19-AttentionGAN:一种加入注意力机制的思路</a></li>
<li class="list-group-item"><a href="cv_blog/0007_ShapeMatchingGAN.html">ICCV19-ShapeMatchingGAN:使用GAN生成艺术字体</a></li>
<li class="list-group-item"><a href="cv_blog/0009_GANDissection.html">ICLR19-GANDissection:GAN的可视化技术</a></li>
<li class="list-group-item"><a href="cv_blog/0012_StarGANv2.html">CVPR20-StarGANv2:latent code+条件GAN</a></li>
<li class="list-group-item"><a href="cv_blog/0013_PGGAN.html">ICLR18-PGGAN:渐进式训练,StyleGAN前作</a></li>
<li class="list-group-item"><a href="cv_blog/0014_StyleGAN.html">CVPR19-StyleGAN:GAN 2.0</a></li>
<li class="list-group-item"><a href="cv_blog/0015_Image2StyleGAN.html">ICCV19-Image2StyleGAN:获取任意图片的latent code</a></li>
</ul>
</div>
<div class="card-header">
<a class="card-link" data-toggle="collapse" href="#collapse3">图像分类模型</a>
</div>
<div id="collapse3" class="collapse" data-parent="#accordion">
<ul class="list-group">
<li class="list-group-item"><a href="#">VGG</a></li>
</ul>
</div>
<div class="card-header">
<a class="card-link" data-toggle="collapse" href="#collapse4">目标检测模型</a>
</div>
<div id="collapse4" class="collapse" data-parent="#accordion">
<ul class="list-group">
<li class="list-group-item"><a href="cv_blog/0010_Yolo系列.html">CVPR16,17-Yolo系列算法</a></li>
</ul>
</div>
</div>
</div>
</div>
</body>
</html>