High-res image reconstruction with latent diffusion models from human brain
I immediately found the results suspect, and think I have found what is actually going on. The dataset it was trained on was 2770 images, minus 982 of those used for validation. I posit that the system did not actually read any pictures from the brains, but simply overfitted all the training images into the network itself. For example, if one looks at a picture of a teddy bear, you&#x27;d get an overfitted picture of another teddy bear from the training dataset instead.<p>The best evidence for this is a picture(1) from page 6 of the paper. Look at the second row. The building generated by &#x27;mind reading&#x27; subject 2 and 4 look strikingly similar, but not very similar to the ground truth! From manually combing through the training dataset, I found a picture of a building that does look like that, and by scaling it down and cropping it exactly in the middle, it overlays rather closely(2) on the output that was ostensibly generated for an unrelated image.<p>If so, at most they found that looking at similar subjects light up similar regions of the brain, putting Stable Diffusion on top of it serves no purpose. At worst it&#x27;s entirely cherry-picked coincidences.<p>1. <a href="https:&#x2F;&#x2F;i.imgur.com&#x2F;ILCD2Mu.png" rel="nofollow">https:&#x2F;&#x2F;i.imgur.com&#x2F;ILCD2Mu.png</a><p>2. <a href="https:&#x2F;&#x2F;i.imgur.com&#x2F;ftMlGq8.png" rel="nofollow">https:&#x2F;&#x2F;i.imgur.com&#x2F;ftMlGq8.png</a>