1. From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
- Author
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Beliy, Roman, Gaziv, Guy, Hoogi, Assaf, Strappini, Francesca, Golan, Tal, and Irani, Michal
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Machine Learning ,Quantitative Biology - Neurons and Cognition ,Statistics - Machine Learning - Abstract
Reconstructing observed images from fMRI brain recordings is challenging. 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. We present a novel approach which, in addition to the scarce labeled data (training pairs), allows to train fMRI-to-image reconstruction networks also on "unlabeled" data (i.e., images without fMRI recording, and fMRI recording without images). The proposed model utilizes both an Encoder network (image-to-fMRI) and a Decoder network (fMRI-to-image). Concatenating these two networks back-to-back (Encoder-Decoder & Decoder-Encoder) allows augmenting the training with both types of unlabeled data. Importantly, it allows training on the unlabeled test-fMRI data. This self-supervision adapts the reconstruction network to the new input test-data, despite its deviation from the statistics of the scarce training data., Comment: *First two authors contributed equally. NeurIPS 2019
- Published
- 2019