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Found 5 papers, 3 papers with code
Robustified ANNs Reveal Wormholes Between Human Category Percepts
Because human category reports (aka human percepts) are thought to be insensitive to those same small-norm perturbations -- and locally stable in general -- this argues that ANNs are incomplete scientific models of human visual perception.
A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity
Reconstructing natural videos from fMRI brain recordings is very challenging, for two main reasons: (i) As fMRI data acquisition is difficult, we only have a limited amount of supervised samples, which is not enough to cover the huge space of natural videos; and (ii) The temporal resolution of fMRI recordings is much lower than the frame rate of natural videos.
More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activity
This is applied to both: (i) the small number of images presented to subjects in an fMRI scanner (images for which we have fMRI recordings - referred to as "paired" data), and (ii) a very large number of natural images with no fMRI recordings ("unpaired data").
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i. e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons.
Learning to aggregate feature representations
We applied our method to the Algonauts2019 fMRI and MEG challenges.
