This work is an update of a previous paper on the same topic published a few years ago. With the dramatic progress in generative modeling, a suite of new quantitative and qualitative techniques to evaluate models has emerged. Although some measures such as Inception Score, Frechet Inception Distance, Precision-Recall, and Perceptual Path Length are relatively more popular, GAN evaluation is not a settled issue and there is still room for improvement. Here, I describe new dimensions that are becoming important in assessing models (e.g. bias and fairness) and discuss the connection between GAN evaluation and deepfakes. These are important areas of concern in the machine learning community today and progress in GAN evaluation can help mitigate them.
翻译:这项工作是对几年前发表的关于同一主题的前一份文件的更新,随着基因模型的突变,一套评估模型的新的定量和定性技术已经出现,尽管一些措施,如 " 启蒙评分 " 、 " 阻击进取距离 " 、 " 精确召回 " 和 " 感知路长 " 等措施相对比较受欢迎,但全球网络评价尚未解决,仍有改进的余地。在这里,我描述了在评估模型(如偏见和公平性)和讨论全球网络评价与深假之间的联系方面正在变得重要的新层面。这些是当今机器学习界的重要关切领域,全球网络评价的进展可以帮助减轻这些影响。