Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond

Deep models have been well-known both for their excellent performance and their black-box nature. In recent years, many interpretation tools have been proposed to explain or reveal the ways that deep models make decisions. To exploit these tools, we first review the state-of-the-art interpretation algorithms within a proposed taxonomy, and present InterpretDL, our open-source implementations of mainstream interpretation algorithms, for explanations of deep models based on PaddlePaddle.