1 code implementation • 12 Dec 2023 • Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris
At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration.
1 code implementation • 1 Jun 2023 • Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris
This emphasis belies the fact that there is always a family of images that are equally compatible with any evoked brain activity pattern, and the fact that many image-generators are inherently stochastic and do not by themselves offer a method for selecting the single best reconstruction from among the samples they generate.
no code implementations • 30 Apr 2023 • Reese Kneeland, Jordyn Ojeda, Ghislain St-Yves, Thomas Naselaris
Past reconstruction algorithms employed brute-force search through a massive library to select candidate images that, when passed through an encoding model, accurately predict brain activity.