Data valuation plays a crucial role in machine learning. Existing data valuation methods have primarily focused on discriminative models, neglecting generative models that have recently gained considerable attention. A very few existing attempts of data valuation method designed for deep generative models either concentrates on specific models or lacks robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. To bridge the gaps, we formulate the data valuation problem in generative models from a similarity-matching perspective. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to provide data valuation for generation tasks. It empowers efficient data valuation through our innovatively similarity matching module, calibrates biased contribution by incorporating image quality assessment, and attributes credits to all training samples based on their contributions to the generated samples. Additionally, we introduce four evaluation criteria for assessing data valuation methods in generative models, aligning with principles of plausibility and truthfulness. GMValuator is extensively evaluated on various datasets and generative architectures to demonstrate its effectiveness.

翻译：数据估值在机器学习中扮演着关键角色。现有的数据估值方法主要集中于判别式模型，忽视了近期备受关注的生成式模型。少数针对深度生成模型设计的现有数据估值尝试，要么局限于特定模型，要么其结果缺乏稳健性。此外，效率方面仍存在明显不足。为弥补这些差距，我们从相似性匹配的角度构建了生成模型中的数据估值问题。具体而言，我们提出了生成模型估值器（GMValuator），这是首个面向生成任务的免训练且与模型无关的数据估值方法。该方法通过我们创新的相似性匹配模块实现高效数据估值，结合图像质量评估校准有偏的贡献度，并根据所有训练样本对生成样本的贡献度进行信用分配。此外，我们提出了四项评估生成模型数据估值方法的标准，这些标准符合合理性与真实性的原则。GMValuator在多种数据集和生成架构上进行了广泛评估，结果验证了其有效性。