Simplex Noise

 So one issue with simple linear interpolation in the latent space of StyleGAN is that the timing of the transitions between the different points changes, because it's dependent on the distance between them in the latent space. So what people are using instead as an alternative is a noise loop interpolation instead of straight linear interpolation.


So this flashed by on the screen in a StyleGAN presentation i was watching, and i thought, oh, are they using Perlin noise to do that? 

Some googling re-activated my brain cells, because Simplex noise is kind of Perlin Noise 2.0.  Better than the original if you want to generate it in high dimensions for sure.  Perlin discussed this in a paper in 2001, which i was very familiar with at the time. 

Of course i'm really, really familiar with the even older 80's paper that started the Perlin noise revolution.  We are seriously cranking the wayback machine this week at HTC.

Stefan Gustavson put together a really good writeup with some code examples titled 'Simple Noise Demystified'.  You can find it here.

Hey, this Perlin noise stuff is all differentiable, i thought to myself.  Maybe we should drop it in some kind of backprop scheme, that would be wild.  I wonder if there is a PyTorch implementation.  Again, the magic of google, with a thanks to Yann LeCun for pointing it out.  Check it ( normal Perlin noise implementation, not simplex one) out here.


So lets take a look at how the simplex interpolation works in just 2D.


So note the circular close pack thing going on.  Now this should immediately get you thinking about Delaunay triangulation.  And then you might think to yourself, hey you know that grid doesn't have to be uniform. Hum...


Found this as well, which is worth keeping a reference to for the discussion of making it all tile seamlessly.

Comments

Post a Comment

Popular posts from this blog

CycleGAN: a GAN architecture for learning unpaired image to image transformations

Image
 Like the Doublemint Twins touting the joys of Doublemint gum, 2 GANs are surely better than 1 GAN.  Especially if we package them together inside of one meta GAN module.  And this is exactly what the CycleGAN architecture does. Have you ever harbored dark secrets of turning a horse into a zebra?  The CycleGAN was developed to do just that. Learn how to turn a horse into a zebra.  And more. Now right away you can notice a difference between the image to image transformation GAN architectures we've been discussing over the last few posts.  Those last few posts described systems that learn from a database of matched input-output image pairs. And if your goal is to turn an edge representation into a nicely filled in continuous tone image, it's easy to build your database of matched input-output image pairs that your GAN system can then learn off of.  Take a continuous tone image (which will be the output of the database pair entry), then run it through an edge detector algorithm. 
Read more

Pix2Pix: a GAN architecture for image to image transformation

Image
 I thought following up yesterday's TraVelGAN post with a Pix2Pix GAN post would be useful to compare what is going on in the 2 architectures.  Two different approaches to the same problem. I stole this Pix2Pix Overview slide below from an excellent deeplearning.ai GAN course (note that they borrowed it from the original paper) because it gives you a good feel for what is going on inside of the Pix2Pix architecture.    Note how the Generator part is very much like an auto-encoder architecture, but rebuilt using the U-Net architecture features (based on skip-connections) that fastai has been discussing in their courses for several years before it became more widely known to the deep learning community at large  (and which originally came from an obscure medical image segmentation paper) . So the Generator in this Pix2Pix GAN is really pretty sophisticated, consisting of a whole image to image auto-encoder network with U-Net skip connections to generate better image quality at highe
Read more

Smart Fabrics

Image
"What makes smart fabrics revolutionary is that they have the ability to do many things that traditional fabrics cannot, including communicate, transform, conduct energy and even grow."  This is a quote from Rebeccah Pailes-Friedman, who is a professor at Pratt with a research focus on developing wearable technology and smart textiles. Enhancing textiles can be viewed through different lens.  Enhancements can be aesthetic, or performance boosting, or durability boosting. 
Read more