English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Neuroscience studies have revealed that the brain encodes visual content andembeds information in neural activity. Recently, deep learning techniques havefacilitated attempts to address visual reconstructions by mapping brainactivity to image stimuli using generative adversarial networks (GANs).However, none of these studies have considered the semantic meaning of latentcode in image space. Omitting semantic information could potentially limit theperformance. In this study, we propose a new framework to reconstruct facialimages from functional Magnetic Resonance Imaging (fMRI) data. With thisframework, the GAN inversion is first applied to train an image encoder toextract latent codes in image space, which are then bridged to fMRI data usinglinear transformation. Following the attributes identified from fMRI data usingan attribute classifier, the direction in which to manipulate attributes isdecided and the attribute manipulator adjusts the latent code to improve theconsistency between the seen image and the reconstructed image. Ourexperimental results suggest that the proposed framework accomplishes twogoals: (1) reconstructing clear facial images from fMRI data and (2)maintaining the consistency of semantic characteristics.

Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency