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.

翻译：数据估值在机器学习中至关重要，因为它有助于增强模型透明度并保护数据属性。现有的数据估值方法主要关注判别模型，而忽略了近年来备受关注的深度生成模型。与判别模型类似，深度生成模型中也迫切需要评估数据贡献。然而，以往的数据估值方法主要依赖判别模型的性能指标，并且需要重新训练模型。因此，这些方法无法直接且高效地应用于当前的深度生成模型，例如生成对抗网络和扩散模型。为弥补这一不足，我们从相似性匹配的角度出发，提出了生成模型中的数据估值问题。具体而言，我们引入了生成模型估值器（GMValuator），这是首个适用于任意生成模型的模型无关方法，旨在为生成任务提供数据估值。我们进行了大量实验，以证明所提方法的有效性。据我们所知，GMValuator是首个为深度生成模型提供无需训练、事后数据估值策略的工作。