Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models

• URL: http://arxiv.org/abs/2109.13531v1
• Date: Tue, 28 Sep 2021 07:18:05 GMT
• Title: Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models
• Authors: Qianmengke Zhao, Ye Wang, Qun Liu
• Abstract summary: We first propose a multi-semantic image recognition model, which enables human beings to understand the decision-making process of the neural network. We then presents a new evaluation index, which can quantitatively assess the model interpretability. This paper also exhibits the relevant baseline performance with current state-of-the-art deep learning models.
• Score: 31.387124252490377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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