GMValuator: Similarity-based Data Valuation for Generative Models
This addresses the need for data valuation in generative models, a domain previously overlooked, offering a practical tool for researchers and practitioners working with image generation tasks.
The authors tackled the problem of data valuation for generative models, which measures the impact of training data on generated datasets, by introducing GMValuator, a training-free and model-agnostic approach that achieved efficient and robust valuation across benchmark and high-resolution datasets with various generative architectures.
Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for deep generative models either concentrate on specific models or lack robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. We formulate the data valuation problem in generative models from a similarity matching perspective to bridge the gaps. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to providing data valuation for image generation tasks. It empowers efficient data valuation through our innovative similarity matching module, calibrates biased contributions 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. GMValuator is extensively evaluated on benchmark and high-resolution datasets and various mainstream generative architectures to demonstrate its effectiveness.