论文标题
改善具有模糊逻辑的生成模型的评估
Improving the Evaluation of Generative Models with Fuzzy Logic
论文作者
论文摘要
评估当前人工智能系统的客观和可解释的指标非常重要，不仅要分析此类系统的当前状态，而且还可以客观地衡量未来的进度。在这项工作中，我们专注于对图像生成任务的评估。我们提出了一种新的方法，称为模糊拓扑影响（FTI），该方法使用拓扑表示形式与模糊逻辑结合了图像集的质量和多样性。与当前的评估方法相比，FTI在评估噪声，模式掉落和模式发明的敏感性的多个实验上显示出更好，更稳定的性能。
Objective and interpretable metrics to evaluate current artificial intelligent systems are of great importance, not only to analyze the current state of such systems but also to objectively measure progress in the future. In this work, we focus on the evaluation of image generation tasks. We propose a novel approach, called Fuzzy Topology Impact (FTI), that determines both the quality and diversity of an image set using topology representations combined with fuzzy logic. When compared to current evaluation methods, FTI shows better and more stable performance on multiple experiments evaluating the sensitivity to noise, mode dropping and mode inventing.
