[mkasu]

Yeah, I thought the same after seeing this. It's kind of a fun use-case of Diffusion models in this context, but as a scientific paper it seems too overselling. Well, it surely is the kind of clickbaity content to anticipate lots of retweets from.

I only skimmed the paper, but from what I understood, it is essentially a diffusion model pre-trained on a handful classes. The brain information is then used to largely "pick which class to generate a random image from".

The paper itself even picked the "better" examples. The supplemental materials show many more results, and many of them are just that, a randomly generated image of the same object class the person was seeing (or, the closest object class available in the training data).

"Reconstruct" seems a pretty bad word choice. I think the results are presented in a way vastly overselling what they actually do. But that's a worrisome trend in most of AI research recently, unfortunately.

(I have a PhD in a field of Applied Machine Learning. I work at a university in Computer Vision.)