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

arXiv:2602.16449v2 Announce Type: replace 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 beha

Source: arXiv cs.LG — read the full report at the original publisher.

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