Deep learning (DL) has been widely used in various fields. However, its black-box nature limits people's understanding and trust in its decision-making process. Therefore, it becomes crucial to research the DL interpretability, which can elucidate the model's decision-making processes and behaviors. This review provides an overview of the current status of interpretability research. First, the DL's typical models, principles, and applications are introduced. Then, the definition and significance of interpretability are clarified. Subsequently, some typical interpretability algorithms are introduced into four groups: active, passive, supplementary, and integrated explanations. After that, several evaluation indicators for interpretability are briefly described, and the relationship between interpretability and model performance is explored. Next, the specific applications of some interpretability methods/models in actual scenarios are introduced. Finally, the interpretability research challenges and future development directions are discussed.

中文翻译：

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深度学习的可解释性研究：文献综述


深度学习 （DL） 已广泛应用于各个领域。然而，它的黑箱性质限制了人们对其决策过程的理解和信任。因此，研究 DL 的可解释性变得至关重要，它可以阐明模型的决策过程和行为。本综述概述了可解释性研究的现状。首先，介绍了深度学习的典型模型、原理和应用。然后，阐明了可解释性的定义和意义。随后，将一些典型的可解释性算法分为四组：主动、被动、补充和综合解释。之后，简要描述了可解释性的几个评估指标，并探讨了可解释性与模型性能之间的关系。接下来，介绍了一些可解释性方法/模型在实际场景中的具体应用。最后，讨论了可解释性研究的挑战和未来的发展方向。