# Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models Source: [Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:34:16.637Z Authors: Qianmengke Zhao, Ye Wang, Qun Liu Published: 2021-09-28T07:18:05.000Z ## Abstract Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand. Therefore, how to evaluate deep neural networks with explanations is still an urgent task. In this paper, we first propose a multi-semantic image recognition model, which enables human beings to understand the decision-making process of the neural network. Then, we presents a new evaluation index, which can quantitatively assess the model interpretability. We also comprehensively summarize the semantic information that affects the image classification results in the judgment process of neural networks. Finally, this paper also exhibits the relevant baseline performance with current state-of-the-art deep learning models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()