stylegan

"First-class and native support for visualization and model debugging with TensorBoard" This is huge. TensorBoard makes it so much easier to understand what's going on when you're training a model over hours or days, and need to tweak hyperparameters. I was recently applying NVidia's stylegan model (https://github.com/NVlabs/stylegan) to a different type of image. Their model uses TF and supports TensorBoard out of the box, so I didn't even try to use pytorch, even though others have shared their implementations (e.g. https://github.com/rosinality/style-based-gan-pytorch).

They likely use https://github.com/NVlabs/stylegan. NVidia recently released it, and suddenly sites like this one and https://thiscatdoesnotexist.com pop up.

I fixed the format for you: • https://github.com/NVlabs/stylegan • https://arxiv.org/abs/1812.04948 • https://www.youtube.com/watch?v=kSLJriaOumA • https://www.lyrn.ai/2018/12/26/a-style-based-generator-archi... • https://www.youtube.com/watch?v=1ct_P3IZow0

https://github.com/NVlabs/stylegan https://arxiv.org/abs/1812.04948 https://www.youtube.com/watch?v=kSLJriaOumA https://www.lyrn.ai/2018/12/26/a-style-based-generator-archi... https://www.youtube.com/watch?v=1ct_P3IZow0

you can download the training set here https://github.com/NVlabs/stylegan#resources it's based on 70k high resolution flickr images. try interpolation on the colab link above so you can be convinced its capturing the features rather than just memorizing

Thanks for all the upvotes! Since I made this site, people have already started to train on datasets beyond just real faces. Turns out it can disentangle pretty much any set of data. Gwern has applied this to anime dataset https://twitter.com/gwern/status/1095131651246575616 Cyril at Google has applied it to artwork https://twitter.com/kikko_fr/status/1094685986691399681 This was to raise awareness for what a talented group of researchers made at Nvidia over the course of 2 years, the latest state of the art for GANs. https://arxiv.org/pdf/1812.04948.pdf (https://github.com/NVlabs/stylegan) Rani Horev wrote up a nice description of the architecture here. https://www.lyrn.ai/2018/12/26/a-style-based-generator-archi... Feel free to experiment with the generations yourself at a colab I made https://colab.research.google.com/drive/1IC0g2oDQ

more details since none were provided: > Recently a talented group of researchers at Nvidia released the current state of the art generative adversarial network, StyleGAN, over at https://github.com/NVlabs/stylegan > I have decided to dig into my own pockets and raise some public awareness for this technology. > Faces are most salient to our cognition, so I've decided to put that specific pretrained model up. Their research group have also included pretrained models for cats, cars, and bedrooms in their repository that you can immediately use. > Each time you refresh the site, the network will generate a new facial image from scratch from a 512 dimensional vector. (https://www.facebook.com/groups/DeepNetGroup/permalink/80536...)

Code: https://github.com/NVlabs/stylegan

This is just to raise awareness for how powerful the generative models have become with the recently open sourced https://github.com/NVlabs/stylegan Based on work from https://arxiv.org/abs/1812.04948