Data valuation is critical in machine learning, as it helps enhance model
transparency and protect data properties. Existing data valuation methods have
primarily focused on discriminative models, neglecting deep generative models
that have recently gained considerable attention. Similar to discriminative
models, there is an urgent need to assess data contributions in deep generative
models as well. However, previous data valuation approaches mainly relied on
discriminative model performance metrics and required model retraining.
Consequently, they cannot be applied directly and efficiently to recent deep
generative models, such as generative adversarial networks and diffusion
models, in practice. To bridge this gap, we formulate the data valuation
problem in generative models from a similarity-matching perspective.
Specifically, we introduce Generative Model Valuator (GMValuator), the first
model-agnostic approach for any generative models, designed to provide data
valuation for generation tasks. We have conducted extensive experiments to
demonstrate the effectiveness of the proposed method. To the best of their
knowledge, GMValuator is the first work that offers a training-free, post-hoc
data valuation strategy for deep generative models.

通过相似度匹配的角度，提出了 Generative Model Valuator (GMValuator) 来作为深度生成模型的第一种模型无关方法，为生成任务提供数据估值策略。此方法是第一种提供无需训练即可进行数据估值的深度生成模型的策略。