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Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images
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Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images Gabriel Sarch Michael Tarr Katerina Fragkiadaki * Leila Wehbe * Carnegie Mellon University * Equal Advising Paper Code Interactive Citation Abstract The alignment between deep neural network (DNN) features and cortical responses currently provides the most accurate quantitative explanation for higher visual areas. At the same time, these model features have been critiqued as uninterpretable explanations, trading one black box (the human brain) for another (a neural network). In this paper, we train networks to directly predict, from scratch, brain responses to images from a large-scale dataset of natural scenes. We then employ "network dissection" (Bau et al., 2017) , ...
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