StyleGAN, 引自原paper 对比传统的generator结构,StyleGAN的style-based generator创新点在以下几点:1)一个新的latent space W,2)一个包含IN的AdaIN结构,3)在上采样过程中加入随机噪声,用于控制随机性高的细节(如,头发末梢的走向、雀斑的位置等)。StyleGAN我参考的代码链接是 github.com/tomguluson92 Z\rightarrow...
最近用到了一些StyleGAN的代码,生成了一些新风格的建筑。本文介绍一下StyleGAN的原理,具体内容还需要去看代码和读paper。 生成性对抗网络(GAN)是机器学习中一个相对较新的概念,于2014年首次引入。他们的目标…
如果我们从正态分布中采样z,我们的模型也会尝试生成缺失的区域,并且其中的比例是不现实的,因为没有具有这种特性的训练数据,生成器将生成较差的图像。因此,映射网络的目的是拆分潜伏表征,并扭曲潜伏空间,使其能够从正态分布中采样。 [Source: Paper] 另外,在每个层次上有单独的输入向量w,使得生成器可以控制不同层次...
Paper:https://arxiv.org/abs/1812.04948 Video:https://youtu.be/kSLJriaOumA Code:https://github.com/NVlabs/stylegan FFHQ:https://github.com/NVlabs/ffhq-dataset Additional material can be found on Google Drive: PathDescription StyleGANMain folder. ...
StyleGAN2paper:Analyzing and Improving the Image Quality of StyleGANcode:https://github.com/NVlabs/stylegan2github.comStyleGAN2 的出现当然是因为StyleGAN存在瑕疵,少量生成的图片有明显的水珠,这个水珠也存在于feature map上,如下图:发现了问题,就开始定位问题,最后找到了,原论文如下,...
The StyleGAN paper, “A Style-Based Architecture for GANs”, was published by NVIDIA in 2018. The paper proposed a new generator architecture for GAN that allows them to control different levels of details of the generated samples from the coarse details (eg. head shape) to the finer details...
Paper:https://arxiv.org/abs/1812.04948 Video:https://youtu.be/kSLJriaOumA Code:https://github.com/NVlabs/stylegan FFHQ:https://github.com/NVlabs/ffhq-dataset Additional material can be found on Google Drive: PathDescription StyleGANMain folder. ...
universome/stylegan-vofficial 366 Tasks Edit AddRemove Video Generation Datasets Edit UCF101MEADFaceForensics Results from the Paper AddRemove Submitresults from this paperto get state-of-the-art GitHub badges and help the community compare results to other papers. ...
这是一批基于StyleGAN2制作的新版人脸生成器,既包含基于旧版重制的网红脸,明星脸,超模脸,萌娃脸和黄种人脸生成器,也新增了两款更具美学意义的混血脸和亚洲美人脸生成器,并附赠有通配的人脸属性编辑器。做了这么多款生成器已经足够用,我将不再尝试做人脸生成器相关的新内容,而是去探索更实用、更能满足用户需求的生...
parameters by using Sifrom the data normalization from the intermediate noise vector (win the figure below refers to weights not intermediate latent space, we are sticking to the same notation as the paper.), where i is the input channel, j is the output channel, and k is the kernel ...