Mind-reading machines

A new AI model sort of reconstructs what you see from brain scans.

Schematics of our reconstruction approach. (A) Model training. We use an adversarial training strategy adopted from Dosovitskiy and Brox (2016b), which consists of 3 DNNs: a generator, a comparator, and a discriminator. The training images are presented to a human subject, while brain activity is measured by fMRI. The fMRI activity is used as an input to the generator. The generator is trained to reconstruct the images from the fMRI activity to be as similar to the presented training images in both pixel and feature space. The adversarial loss constrains the generator to generate reconstructed images that fool the discriminator to classify them as the true training images. The discriminator is trained to distinguish between the reconstructed image and the true training image. The comparator is a pre-trained DNN, which was trained to recognize the object in natural images. Both the reconstructed and true training images are used as an input to the comparator, which compares the image similarity in feature space. (B) Model test. In the test phase, the images are reconstructed by providing the fMRI activity of the test image as the input to the generator. (Shen et al, 2018)

Check the Journal Article here

Reference: Shen, Guohua, Kshitij Dwivedi, Kei Majima, Tomoyasu Horikawa, and Yukiyasu Kamitani. “End-to-End Deep Image Reconstruction from Human Brain Activity.” BioRxiv, February 27, 2018, 272518. https://doi.org/10.1101/272518.