Editing Faces using Artificial Intelligence
Link to Notebooks:
https://drive.google.com/open?id=1LBWcmnUPoHDeaYlRiHokGyjywIdyhAQb
Link to the StyleGAN paper: https://arxiv.org/abs/1812.04948
Link to GAN blogpost: http://hunterheidenreich.com/blog/gan-objective-functions/
If you want to support this channel, here is my patreon link:
https://patreon.com/ArxivInsights --- You are amazing!! ;)
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
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This episode covers one of the greatest ideas in Deep Learning of the past couple of years: Generative Adversarial Networks.
I first explain how a generative adversarial network (GAN) really works. After this general overview, we go into the specific objective function that is optimized during training. We then dive into Nvidia's StyleGAN model and learn how we can manipulate it's latent space to morph arbitrary images of faces.
This video comes with a complete Google Colab notebook to reproduce & play with all the examples shown in the video!
::Chapters::
00:00 Intro
02:55 Video overview
03:35 Introduction to GANs
05:40 5 min Deepdive on the Training Objective for GANs
10:07 State-of-the-art GAN techniques: StyleGAN
14:40 Manipulating the latent space of GANs
Link to Notebooks:
https://drive.google.com/open?id=1LBWcmnUPoHDeaYlRiHokGyjywIdyhAQb
Link to the StyleGAN paper: https://arxiv.org/abs/1812.04948
Link to GAN blogpost: http://hunterheidenreich.com/blog/gan-objective-functions/
If you want to support this channel, here is my patreon link:
https://patreon.com/ArxivInsights — You are amazing!! 😉
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
——————————–
This episode covers one of the greatest ideas in Deep Learning of the past couple of years: Generative Adversarial Networks.
I first explain how a generative adversarial network (GAN) really works. After this general overview, we go into the specific objective function that is optimized during training. We then dive into Nvidia’s StyleGAN model and learn how we can manipulate it’s latent space to morph arbitrary images of faces.
This video comes with a complete Google Colab notebook to reproduce & play with all the examples shown in the video!
::Chapters::
00:00 Intro
02:55 Video overview
03:35 Introduction to GANs
05:40 5 min Deepdive on the Training Objective for GANs
10:07 State-of-the-art GAN techniques: StyleGAN
14:40 Manipulating the latent space of GANs
