Connecting Generative Adversarial Network and Actor-Critic Methods.pdf

用于Pytorch的简单StyleGan2 基于的Stylegan2的简单Pytorch实现，可以从命令行进行完全培训，无需编码。 下面是一些不存在的花。 这些手也不 这些城市也没有 这些名人也没有（由训练） 安装 您将需要一台装有GPU和CUDA的计算机。 然后pip这样安装软件包 $ pip install stylegan2_pytorch 如果您使用的是Windows计算机，则可以使用以下命令。 $ conda install pytorch torchvision -c python $ pip install stylegan2_pytorch 利用 $ styleg

Unity-Synth实验 一组使用Unity3D的OnAudioFilterRead（）的简单生成声音引擎。 我从大量借用开始，并建议检查该存储库以获得更多的模块化实现。 最终，我为自己重新组织了工作，因此每个引擎都相当独立。 如何使用 Scripts / SoundEngines /中的脚本是有趣的部分。 将其中之一与AudioSource一起添加到gameObject即可。 您将需要制作一个脚本，该脚本调用KeyOn（）方法来激活声音。 这些检查员的数量很少，但是应该有很多可以使用和扩展的检查员。 包含的场景显示了音频与游戏对象互动的一些方式。 希望这3个演示场景能给大家一个关于如何以

注意：手机拍摄版本！！！后面一张张调整的，有页面不清楚！！！ 作者：Hartmut Bohnacker, Benedikt Groß, Julia Laub 本书最初为09年出版的德文版，12年由 Claudius Lazzeroni 译为英文。 主页：http://www.generative-gestaltung.de/1/ 介绍：摘录自Processing.org This book is extraordinary; the design is clear and the production quality is fantastic. This is the design book about Processing that we've hoped for. Unlike most other Processing books, it doesn't discuss programming basics so it's free to start with exciting examples. The publisher promotes, "Generative design is a revolutionary new method of creating artwork, models, and animations from sets of rules, or algorithms. By using accessible programming languages such as Processing, artists and designers are producing extravagant, crystalline structures that can form the basis of anything from patterned textiles and typography to lighting, scientific diagrams, sculptures, films, and even fantastical buildings. Opening with a gallery of thirty-five illustrated case studies, Generative Design takes users through specific, practical instructions on how to create their own visual experiments by combining simple-to-use programming codes with basic design principles. A detailed handbook of advanced strategies provides visual artists with all the tools to achieve proficiency. Both a how-to manual and a showcase for recent work in this exciting new field, Generative Design is the definitive study and reference book that designers have been waiting for."

两年一度的计算机视觉国际顶级会议 International Conference on Computer Vision（ICCV 2017）在意大利威尼斯开幕。Google Brain 研究科学家Ian Goodfellow在会上作为主题为《生成对抗网络(Generative Adversarial Networks)》的Tutorial 最新演讲, 介绍了GAN的原理和最新的应用。

Learning Generative Adversarial Networks_Next generation deep learning simplified First published: October 2017 | 203页 | pdf格式

条件生成对抗网络（CGAN）， tensorflow实现

This book is for data scientists, machine learning (ML) developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and a working knowledge of the Python programming language will help you get the most out of the book.

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