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所在平台: Udemy |
课程主页: https://www.udemy.com/course/generative-adversarial-networks-gans-complete-guide/
课程评论：没有评论
课程名称：生成对抗网络（GANs）：完全指南 课程概述：生成对抗网络（GANs）是深度学习和计算机视觉领域中最现代和令人着迷的技术之一。由于其能够生成虚假内容，因此受到广泛关注。经典示例包括生成不存在于现实世界中的人脸以用于电视节目。这项技术被认为是人工智能领域的一次革命，能够产生高质量结果，始终是最受欢迎和相关的话题之一。 在本课程中，您将学习生成对抗网络的基本直觉及其最新架构的实际应用。该课程被认为是一个完整的指南，涵盖从基础概念到现代先进技术的所有内容，使您最终能够掌握构建自己项目所需的所有工具。您将逐步实现以下一些项目： - 创建从0到9的数字 - 将卫星图像转化为地图样式图像，如谷歌地图 - 将绘画转换为高质量照片 - 使用马的图像生成斑马图像 - 使用梵高、塞尚和浮世绘等著名艺术家的绘画进行风格迁移 - 提高低质量图像的分辨率（超分辨率） - 生成高质量的深度伪造（虚假人脸） - 根据文本描述生成图像 - 修复旧照片 - 补全图像缺失部分 - 在不同环境中的人脸互换 为实现这些项目，您将学习多种不同架构的GANs，如：DCGAN（深度卷积生成对抗网络）、WGAN（水斯坦GAN）、WGAN-GP（水斯坦GAN-梯度惩罚）、cGAN（条件GAN）、Pix2Pix（图像到图像）、CycleGAN（循环一致对抗网络）、SRGAN（超分辨率GAN）、ESRGAN（增强型超分辨率GAN）、StyleGAN（基于风格的生成器架构）、VQ-GAN（矢量量化生成对抗网络）、CLIP（对比语言-图像预训练）、BigGAN、GFP-GAN（生成面部优先GAN）、Unlimited GAN（无界限）和SimSwap（简单互换）。 在课程中，我们将使用Python编程语言和Google Colab在线平台，因此您无需担心在自己的机器上安装和配置库！课程包括100多节讲座，16小时的视频内容！
GANs (Generative Adversarial Networks) are considered one of the most modern and fascinating technologies within the field of Deep Learning and Computer Vision. They have gained a lot of attention because they can create fake content. One of the most classic examples is the creation of people who do not exist in the real world to be used to broadcast television programs. This technology is considered a revolution in the field of Artificial Intelligence for producing high quality results, remaining one of the most popular and relevant topics.In this course you will learn the basic intuition and mainly the practical implementation of the most modern architectures of Generative Adversarial Networks! This course is considered a complete guide because it presents everything from the most basic concepts to the most modern and advanced techniques, so that in the end you will have all the necessary tools to build your own projects! See below some of the projects that you are going to implement step by step:Creating of digits from 0 to 9Transforming satellite images into map images, like Google Maps styleConvert drawings into high-quality photosCreate zebras using horse imagesTransfer styles between images using paintings by famous artists such as Van Gogh, Cezanne and Ukiyo-eIncrease the resolution of low quality images (super resolution)Generate deepfakes (fake faces) with high qualityCreate images through textual descriptionsRestore old photosComplete missing parts of imagesSwap the faces of people who are in different environmentsTo implement the projects, you will learn several different architectures of GANs, such as: DCGAN (Deep Convolutional Generative Adversarial Network), WGAN (Wassertein GAN), WGAN-GP (Wassertein GAN-Gradient Penalty), cGAN (conditional GAN), Pix2Pix (Image-to-Image), CycleGAN (Cycle-Consistent Adversarial Network), SRGAN (Super Resolution GAN), ESRGAN (Enhanced Super Resolution GAN), StyleGAN (Style-Based Generator Architecture for GANs), VQ-GAN (Vector Quantized Generative Adversarial Network), CLIP (Contrastive Language-Image Pre-training), BigGAN, GFP-GAN (Generative Facial Prior GAN), Unlimited GAN (Boundless) and SimSwap (Simple Swap).During the course, we will use the Python programming language and Google Colab online, so you do not have to worry about installing and configuring libraries on your own machine! More than 100 lectures and 16 hours of videos!
