Brain Decodes Deep Nets

• URL: http://arxiv.org/abs/2312.01280v2
• Date: Sat, 30 Mar 2024 03:30:18 GMT
• Title: Brain Decodes Deep Nets
• Authors: Huzheng Yang, James Gee, Jianbo Shi,
• Abstract summary: We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images.
• Score: 9.302098067235507
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, FactorTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (literally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides insight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchically organized network suffer less from catastrophic forgetting after fine-tuned.

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