解决生成模型评估中的近邻偏差问题，提升距离度量可靠性
GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
- 基于ICDM思想提出GICDM，校正真实与生成数据的邻域估计
- 多尺度扩展显著改善实际表现，使评估指标更稳定可靠
- 适用于需精准评估生成质量的研究者，尤其关注人类感知对齐
生成模型评估常依赖高维嵌入空间中的样本距离。我们发现这些空间中的数据表示受近邻偏差（hubness）现象影响，扭曲了最近邻关系并导致距离度量产生偏差。基于经典迭代上下文差异度量（ICDM），我们提出生成ICDM（GICDM），用于校正真实数据与生成数据的邻域估计。引入多尺度扩展以优化实际表现。在合成与真实基准上的大量实验表明，GICDM能有效缓解近邻偏差带来的评估失败，恢复度量的可靠性，并提升与人类评估的一致性。
原文摘要 · Abstract (English)
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.
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