Generative adversarial networks (GANs) are increasingly attracting attention
in the computer vision, natural language processing, speech synthesis and
similar domains. However, evaluating the performance of GANs is still an open
and challenging problem. Existing evaluation metrics primarily measure the
dissimilarity between real and generated images using automated statistical
methods. They often require large sample sizes for evaluation and do not
directly reflect human perception of image quality. In this work, we introduce
an evaluation metric called Neuroscore, for evaluating the performance of GANs,
that more directly reflects psychoperceptual image quality through the
utilization of brain signals. Our results show that Neuroscore has superior
performance to the current evaluation metrics in that: (1) It is more
consistent with human judgment; (2) The evaluation process needs much smaller
numbers of samples; and (3) It is able to rank the quality of images on a per
GAN basis. A convolutional neural network (CNN) based neuro-AI interface is
proposed to predict Neuroscore from GAN-generated images directly without the
need for neural responses. Importantly, we show that including neural responses
during the training phase of the network can significantly improve the
prediction capability of the proposed model. Codes and data can be referred at
this link: this https URL

本研究提出了一种 Neuroscore 测评策略，该策略通过利用脑信号更直接地反映心理感知图像质量，优于现有算法并能够基于单个 GAN 排名图像质量，同时，本研究还介绍了基于卷积神经网络的神经 - 人工智能接口，可以直接从 GAN 生成的图像中预测 Neuroscore。