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所在平台: Udemy |
课程主页: https://www.udemy.com/course/high-resolution-generative-adversarial-networks/
课程评论：没有评论
Coursera 课程：高分辨率生成对抗网络 (GAN) 本课程旨在教授构建先进生成对抗网络 (GAN) 所需的知识。许多尝试过 GAN 的人都知道，生成 MNIST 数字相对容易，但要生成高分辨率的逼真图像则难得多。本课程将弥合这一差距，指导您创建和训练可用于现实世界应用的 GAN。 由于训练高分辨率网络计算成本高昂，本课程还将教授如何将训练分布到多个 GPU 或 TPU 上。课程将利用 Google Colab 提供的免费 Google TPU 硬件，使学生能够以零硬件成本训练高达 512x512 分辨率的生成器。 课程内容基于 ProGAN、StyleGAN 和 StyleGAN 2 这几篇产生了开创性和令人惊叹结果的论文。学生将使用 Flicker Faces HD 数据集来复现这些论文的成果。 此外，课程还将介绍如何生成无限量的独特图像，并通过感知路径长度过滤器来筛选出高质量的图像。学生还将学习如何生成两个生成图像之间的平滑插值，创造出有趣的视觉效果。
This course covers the fundamentals necessary for a state-of-the-art GAN. Anyone who experimented with GANs on their own knows that it's easy to throw together a GAN that spits out MNIST digits, but it's another level of difficulty entirely to produce photorealistic images at a resolution higher than a thumbnail.This course comprehensively bridges the gap between MNIST digits and high-definition faces. You'll create and train a GAN that can be used in real-world applications.And because training high-resolution networks of any kind is computationally expensively, you'll also learn how to distribute your training across multiple GPUs or TPUs. Then for training, we'll leverage Google's TPU hardware for free in Google Colab. This allows students to train generators up to 512x512 resolution with no hardware costs at all.The material for this course was pulled from the ProGAN, StyleGAN, and StyleGAN 2 papers which have produced ground-breaking and awe-inspiring results. We'll even use the same Flicker Faces HD dataset to replicate their results.Finally, what GAN course would be complete without having some fun with the generator? Students will learn not only how to generate an infinite quantity of unique images, but also how to filter them to the highest-quality images by using a perceptual path length filter. You'll even learn how to generate smooth interpolations between two generated images, which make for some really interesting visuals.
