Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks

• URL: http://arxiv.org/abs/2408.13397v1
• Date: Fri, 23 Aug 2024 22:44:21 GMT
• Title: Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
• Authors: Xuran Hu, Mingzhe Zhu, Zhenpeng Feng, Miloš Daković, Ljubiša Stanković,
• Abstract summary: The "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. We introduce a perturbation-based interpretation guided by feature coalitions, which leverages deep information of network to extract correlated features.
• Score: 0.1398098625978622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The inherent "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. Recently, explainable AI (XAI) has garnered increasing attention from researchers. Several perturbation-based interpretations have emerged. However, these methods often fail to adequately consider feature dependencies. To solve this problem, we introduce a perturbation-based interpretation guided by feature coalitions, which leverages deep information of network to extract correlated features. Then, we proposed a carefully-designed consistency loss to guide network interpretation. Both quantitative and qualitative experiments are conducted to validate the effectiveness of our proposed method. Code is available at github.com/Teriri1999/Perturebation-on-Feature-Coalition.

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