GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation

• URL: http://arxiv.org/abs/2602.16449v1
• Date: Wed, 18 Feb 2026 13:33:54 GMT
• Title: GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
• Authors: Nicolas Salvy, Hugues Talbot, Bertrand Thirion,
• Abstract summary: Generative model evaluation commonly relies on high-dimensional embedding spaces to compute distances between samples.<n>We show that dataset representations in these spaces are affected by the hubness phenomenon.<n>We introduce Generative ICDM, a method to correct neighborhood estimation for both real and generated data.
• Score: 30.08046476442414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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