“Leaning Tower of Pizza”, BigGAN steered by CLIP using Big SleepI wrote earlier about DALL-E [https://www.aiweirdness.com/the-drawings-of-dall-e/], an image
generating algorithm recently developed by OpenAI. One part of DALL-E
[https://openai.com/blog/dall-e/]’s success is another algorithm called
There’s a story I tell in my book [https://youlooklikeathing.com] because it’s a
great illustration of how AI gets the wrong idea about what problem we’re asking
it to solve:
Researchers at the University of Tuebingen trained a neural net to recognize
images
[https://medium.com/
The other day someone told me about a program
[https://deepai.org/machine-learning-model/text2img] that will generate scenes
to match a text description. I’m always excited to test out algorithms like this
because the task of “draw anything a human asks for” is so hard that even
Last week I attempted to illustrate some neural net-generated racehorses by
turning to another neural net - this time, one that generates images, called
BigGAN [https://aiweirdness.com/post/182322518157/welcome-to-latent-space].
Using Joel Simon’s ganbreeder.app [http://ganbreeder.app] interface, I’m able to
see what
I’ve written before about BigGAN
[http://aiweirdness.com/post/178619746932/imaginary-worlds-dreamed-by-biggan],
an image-generating neural net that Google trained recently. It generates its
best images for each of the 1,000 different categories
[https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a] in the standard ImageNet
dataset, from
Writing to you from deep inside the Uncanny Valley, I present to you some
hand-selected images generated by an algorithm called BigGAN
[https://arxiv.org/abs/1809.11096]. This algorithm looks at example images of a
bunch of different objects and then tries its best to figure out how
These are some of the most amazing generated images I’ve ever seen. Introducing
BigGAN, a neural network that generates high-resolution, sometimes
photorealistic, imitations of photos it’s seen. None of the images below are
real - they’re all generated by BigGAN.
Preprints of the BigGAN paper are here