A Neuro-AI Interface for Evaluating Generative Adversarial Networks
This addresses the challenge of evaluating GAN performance for researchers and practitioners in computer vision and related fields, offering a more human-aligned metric, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of evaluating Generative Adversarial Networks (GANs) by introducing Neuroscore, a metric that uses brain signals to reflect human perception of image quality, resulting in superior performance over existing metrics with higher consistency to human judgment, smaller sample sizes, and per-GAN ranking ability.
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: https://github.com/villawang/Neuro-AI-Interface.