Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
<|reference_start|>Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks: 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.<|reference_end|>
@article{hu2024perturbation,
    title={Perturbation on Feature Coalition: Towards Interpretable Deep Neural
  Networks},
    author={Xuran Hu, Mingzhe Zhu, Zhenpeng Feng, Milov{s} Dakovi'c, Ljubiv{s}a
  Stankovi'c},
    journal={arXiv preprint arXiv:2408.13397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13397},
    primaryClass={cs.CV cs.LG}
}