Generative model evaluation commonly relies on high-dimensional embedding spaces to compute distances between samples. We show that dataset representations in these spaces are affected by the hubness phenomenon, which distorts nearest neighbor relationships and biases distance-based metrics. Building on the classical Iterative Contextual Dissimilarity Measure (ICDM), we introduce Generative ICDM (GICDM), a method to correct neighborhood estimation for both real and generated data. We introduce a multi-scale extension to improve empirical behavior. Extensive experiments on synthetic and real benchmarks demonstrate that GICDM resolves hubness-induced failures, restores reliable metric behavior, and improves alignment with human judgment.

翻译：生成模型评估通常依赖于高维嵌入空间来计算样本间距离。我们发现，这些空间中的数据集表示会受到中心性现象的影响，该现象会扭曲最近邻关系并导致基于距离的指标产生偏差。基于经典的迭代上下文相异性度量（ICDM），我们提出了生成式ICDM（GICDM），一种用于校正真实数据与生成数据邻域估计的方法。我们进一步引入了多尺度扩展以改进其经验性能。在合成与真实基准测试上的大量实验表明，GICDM能够解决由中心性引起的评估失效问题，恢复指标的可靠行为，并提升与人类判断的一致性。