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 assessment.

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的距离。我们证明，这些空间中的数据集表示受到枢纽性现象的影响，这会扭曲最近邻关系并使基于距离的度量产生偏差。基于经典的迭代上下文不相似性度量（ICDM），我们提出了生成式ICDM（GICDM），这是一种对真实数据和生成数据进行邻域估计校正的方法。我们引入了一种多尺度扩展以改进实证表现。在合成与真实基准上的大量实验表明，GICDM能够解决枢纽性导致的失败，恢复可靠的度量行为，并提升与人类评估的一致性。