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掌握GAN模型经典算法及其论文核心思想

熟练使用深度学习框架构建各大经典架构

掌握对抗生成网络当下主流算法框架及其应用

熟练基于论文构建自己的项目

population (esp. of a group of people)

深度学习方向的同学们
Courses

对抗生成网络实战系列主要包括三大核心内容：1.经典GAN论文解读；2.源码复现解读；3.项目实战应用。全程实战解读各大经典GAN模型构建与应用方法，通俗讲解论文中核心知识点与整体网络模型架构，从数据预处理与环境配置开始详细解读项目源码及其应用方法。提供课程所需全部数据，代码，PPT。

Course Catalog

第一章：对抗生成网络架构原理与实战解析
课程介绍（数据代码下载————->需要PC登录） 07:48
对抗生成网络通俗解释 08:24
GAN网络组成 05:14
损失函数解释说明 10:05
数据读取模块 08:26
生成与判别网络定义 08:39

第二章：基于CycleGan开源项目实战图像合成
CycleGan网络所需数据 06:50
CycleGan整体网络架构 10:03
PatchGan判别网络原理 04:40
Cycle开源项目简介 07:07
数据读取与预处理操作 10:17
生成网络模块构造 12:12
判别网络模块构造 05:02
损失函数：identity loss计算方法 09:12
生成与判别损失函数指定 11:40
额外补充：VISDOM可视化配置 05:54

第三章：stargan论文架构解析
stargan效果演示分析 06:13
网络架构整体思路解读 09:00
建模流程分析 07:08
V1版本存在的问题及后续改进思路 06:34
V2版本整体网络架构 08:01
编码器训练方法 06:23
损失函数公式解析 08:31
训练过程分析 04:50

第四章：stargan项目实战及其源码解读
项目配置与数据源下载 05:11
测试效果演示 06:17
项目参数解析 04:28
生成器模块源码解读 07:46
所有网络模块构建实例 07:18
数据读取模块分析 10:29
判别器损失计算 05:51
损失计算详细过程 07:04
生成模块损失计算 10:58
测试模块效果与实验分析 04:34

第五章：基于starganvc2的变声器论文原理解读
论文整体思路与架构解读 07:26
VCC2016输入数据 07:26
语音特征提取 11:38
生成器模型架构分析 05:11
InstanceNorm的作用解读 07:30
AdaIn的目的与效果 05:07
判别器模块分析 13:10

第六章：starganvc2变声器项目实战及其源码解读
数据与项目文件解读 07:00
环境配置与工具包安装 08:02
数据预处理与声音特征提取 13:46
生成器构造模块解读 09:07
下采样与上采样操作 07:54
starganvc2版本标签输入分析 06:00
生成器前向传播维度变化 07:03
判别器模块解读 07:49
论文损失函数 08:02
源码损失计算流程 06:07
测试模块-生成转换语音 09:05

第七章：图像超分辨率重构实战
论文概述 05:17
网络架构 08:47
数据与环境配置 07:49
数据加载与配置 08:34
生成模块 07:32
判别模块 06:57
VGG特征提取网络 06:18
损失函数与训练 11:47
测试模块 07:59

第八章：基于GAN的图像补全实战
论文概述 10:02
网络架构 11:03
细节设计 08:01
论文总结 09:19
数据与项目概述 10:07
参数基本设计 09:14
网络结构配置 12:21
网络迭代训练 16:54
测试模块 05:24

第九章：基础补充-PyTorch卷积模型实例
Convolutional network parameter definitions 07:21
Network Process Interpretation 07:26
Vision module functionality explained 05:10
Categorization task dataset definition and configuration 06:27
The role of image enhancement 04:51
Data Preprocessing and Data Enhancement Module 09:25
Batch数据制作 08:37
The goal of transfer learning 05:31
Transfer Learning Strategies 07:11
Loading the trained network model 09:54
Optimizer module configuration 05:14
Implementing the training module 08:15
Training results and model saving 09:31
Loading models to make predictions on test data 09:10
Additional Additions - Resnet Thesis Interpretation 11:47
Additional Additions - Resnet Network Architecture Explained 08:26

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