Journal Article


Tomas E. Ward
Alan F. Smeaton
Graham Healy
Zhengwei Wang


Computer Science

brain computer interface neuroscore neuro ai interface rapid serial visual presentation machine learning artificial intelligence neuroscience generative adversarial networks

Use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation (2019)

Abstract There is a growing interest in using generative adversarial networks (GANs) to produce image content that is indistinguishable from real images as judged by a typical person. A number of GAN variants for this purpose have been proposed; however, evaluating GAN performance is inherently difficult because current methods for measuring the quality of their output are not always consistent with what a human perceives. We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images. This technique we call a neuro-AI interface, as it provides an interface between a human’s neural systems and an AI process. In this paper, we first compare the three most widely used metrics in the literature for evaluating GANs in terms of visual quality and compare their outputs with human judgments. Secondly, we propose and demonstrate a novel approach using neural signals and rapid serial visual presentation (RSVP) that directly measures a human perceptual response to facial production quality, independent of a behavioral response measurement. The correlation between our proposed Neuroscore and human perceptual judgments has Pearson correlation statistics: r(48) = − 0.767, p = 2.089e − 10. We also present the bootstrap result for the correlation i.e., p ≤ 0.0001. Results show that our Neuroscore is more consistent with human judgment compared with the conventional metrics we evaluated. We conclude that neural signals have potential applications for high-quality, rapid evaluation of GANs in the context of visual image synthesis.
Collections Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Subject = Computer Science: Artificial intelligence
Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Biological Sciences: Neuroscience
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Tomas E. Ward, Alan F. Smeaton, Graham Healy, Zhengwei Wang

Experts in our system

Tomas Ward
Maynooth University
Total Publications: 189
Alan F. Smeaton
Dublin City University
Total Publications: 492
Graham Healy
Dublin City University
Total Publications: 34
Zhengwei Wang
Dublin City University
Total Publications: 11