62 citations · 75 across the 7 of their papers we have counts for
7 papers
Noninvasive precision modulation of high-level neural population activity via natural vision perturbations
Guy Gaziv, Sarah Goulding, Ani Ayvazian-Hancock +2
Precise control of neural activity -- modulating target neurons deep in the brain while leaving nearby neurons unaffected -- is an outstanding challenge in neuroscience, generally…
L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement
Morgan B. Talbot, Gabriel Kreiman, James J. DiCarlo +1
The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods -…
Robustified ANNs Reveal Wormholes Between Human Category Percepts
Guy Gaziv, Michael J. Lee, James J. DiCarlo
The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations. Because human category reports (aka huma…
A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity
Ganit Kupershmidt, Roman Beliy, Guy Gaziv +1
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 supe…
More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activity
Guy Gaziv, Michal Irani
In the past few years, significant advancements were made in reconstruction of observed natural images from fMRI brain recordings using deep-learning tools. Here, for the first tim…
Learning to aggregate feature representations
Guy Gaziv
The Algonauts challenge requires to construct a multi-subject encoder of images to brain activity. Deep networks such as ResNet-50 and AlexNet trained for image classification are…
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Roman Beliy, Guy Gaziv, Assaf Hoogi +3
Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their correspondin…