Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity
| Autor: | Guy Gaziv, Niv Granot, Francesca Strappini, Tal Golan, Roman Beliy, Michal Irani, Assaf Hoogi |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Brain activity and meditation Cognitive Neuroscience media_common.quotation_subject Brain Self-Supervised learning Decoding Encoding fMRI Image reconstruction Classificationvision Pattern recognition Iterative reconstruction Magnetic Resonance Imaging Semantics Consistency (database systems) Neurology Perception Image Processing Computer-Assisted Humans Artificial intelligence Biological plausibility Neural Networks Computer business media_common |
| Zdroj: | bioRxiv |
| ISSN: | 1095-9572 |
| Popis: | Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We present a novel self-supervised approach that goes well beyond the scarce paired data, for achieving both: (i) state-of-the art fMRI-to-image reconstruction, and (ii) first-ever large-scale semantic classification from fMRI responses. By imposing cycle consistency between a pair of deep neural networks (from image-to-fMRI & from fMRI-to-image), we train our image reconstruction network on a large number of "unpaired" natural images (images without fMRI recordings) from many novel semantic categories. This enables to adapt our reconstruction network to a very rich semantic coverage without requiring any explicit semantic supervision. Specifically, we find that combining our self-supervised training with high-level perceptual losses, gives rise to new reconstruction & classification capabilities. In particular, this perceptual training enables to classify well fMRIs of never-before-seen semantic classes, without requiring any class labels during training. This gives rise to: (i) Unprecedented image-reconstruction from fMRI of never-before-seen images (evaluated by image metrics and human testing), and (ii) Large-scale semantic classification of categories that were never-before-seen during network training. Such large-scale (1000-way) semantic classification from fMRI recordings has never been demonstrated before. Finally, we provide evidence for the biological consistency of our learned model. |
| Databáze: | OpenAIRE |
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