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Data valuation plays a crucial role in machine learning. Existing datavaluation methods have primarily focused on discriminative models, neglectinggenerative models that have recently gained considerable attention. A very fewexisting attempts of data valuation method designed for deep generative modelseither 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 asimilarity-matching perspective. Specifically, we introduce Generative ModelValuator (GMValuator), the first training-free and model-agnostic approach toprovide data valuation for generation tasks. It empowers efficient datavaluation through our innovatively similarity matching module, calibratesbiased contribution by incorporating image quality assessment, and attributescredits to all training samples based on their contributions to the generatedsamples. Additionally, we introduce four evaluation criteria for assessing datavaluation methods in generative models, aligning with principles ofplausibility and truthfulness. GMValuator is extensively evaluated on variousdatasets and generative architectures to demonstrate its effectiveness.

GMValuator: Similarity-based Data Valuation for Generative Models