Abstract:
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. We propose a novel metric, called Fuzzy Topology Impact (FTI), that assesses both the quality and diversity of a generated set using topological representations combined with fuzzy logic. In our synthetic experiments, FTI consistently outperforms current evaluation methods in terms of stability and sensitivity to detect drops in quality and diversity in the generated set, both on image and text generation tasks. Moreover, FTI shows a high degree of correlation to human evaluation on unconditional language generation.
