Knowledge constitutes the accumulated understanding and experience that humans use to gain insight into the world. In deep learning, prior knowledge is essential for mitigating shortcomings of data-driven models, such as data dependence, generalization ability, and compliance with constraints. To enable efficient evaluation of the worth of knowledge, we present a framework inspired by interpretable machine learning. Through quantitative experiments, we assess the influence of data volume and estimation range on the worth of knowledge. Our findings elucidate the complex relationship between data and knowledge, including dependence, synergistic, and substitution effects. Our model-agnostic framework can be applied to a variety of common network architectures, providing a comprehensive understanding of the role of prior knowledge in deep learning models. It can also be used to improve the performance of informed machine learning, as well as distinguish improper prior knowledge.

翻译：知识是人类用于洞察世界的累积理解与经验。在深度学习中，先验知识对于弥补数据驱动模型的缺陷至关重要，例如数据依赖性、泛化能力以及约束条件遵从性。为了高效评估知识的价值，我们提出了一种受可解释机器学习启发的框架。通过定量实验，我们评估了数据量和估计范围对知识价值的影响。研究结果阐明了数据与知识之间复杂的相互关系，包括依赖效应、协同效应和替代效应。我们的模型无关框架可应用于多种常见网络架构，从而全面理解先验知识在深度学习模型中的作用。该框架还可用于改进知识增强机器学习的性能，并识别不恰当的先验知识。