iyaja

Hi everyone. I just published a new blog post which talks about the evolution of GANs over the last few years. You can check it out here.

I think it's fascinating to see sample images generated from these models side by side. It really does give a sense of how fast this field has progressed. In just five years, we've gone from blurry, grayscale pixel arrays that vaguely resemble human faces to thispersondoesnotexist, which can easily fool most people on first glance.

Apart from image samples, I've also included links to papers, code, and other learning resources for each model. So this article could be an excellent place to start if you're a beginner looking to catch up with the latest GAN research.

I hope you enjoy it!

Hi everyone. I'm the author of this article.

In case you didn't read it yet, I used a StyleGAN to interpolate between a few popular characters from HBO's Game of Thrones series.

Here's something interesting to note: All the results (images and animations) were generated from Nvidia's StyleGAN that was pretrained on the FFHQ dataset, with absolutely no fine-tuning.

Instead, to make StyleGAN work for Game of Thrones characters, I used another model that maps images onto StyleGAN's latent space. I gave it images of Jon, Daenerys, Jaime, etc. and got latent vectors that when fed through StyleGAN, recreate the original image.

With the latent vectors for the images in hand, it's really to modify them in all the ways described in the StyleGAN paper (style mixing, interpolations, etc.) as well as through simple arithmetic in the latent space (such as shifting the latent vector in the "smiling direction").

As a bonus, since there's no StyleGAN training involved, all the steps that I just mentioned can be executed extremely fast.

Hi everyone. I'm the author. There's one more I thing I wanted to add: a good reason you should try using some sort of hyperparameter search, even you think it's a complete waste of time and compute, is for reproducibility.

This probably applies more to open-source academic contributions, where you're trying to help your fellow practitioners recreate and use your models, as opposed to a corporate setting, where reproducibility would be the equivalent of getting fired.

Recently, I was trying to train a ResNet to beat the top Stanford DAWNBench entry (spoiler alert: I did, but by less than a second). Initially, I blindly tried manually tuning the learning rate, batch size, etc. without even reading the original model's guidelines.

After actually going through a blog post written by the David C Page (the guy with the top DAWNBench entry), I saw that he tried varying the hyperparameters himself and that the ones that were set by default in the code were what he found to be optimal.

That saved me a lot of time and let me focus on other things like what hardware to use.

I think the lesson here is that if more researchers perform and publish the results of some basic hyperparameter optimization, it would really save the world a whole lot of epochs.