MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks
Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Minhui Xue, Dongxiao Zhu, Kim-Kwang Raymond Choo
Abstract
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.
BibTeX
@article{Zhu_Chen_Zhang_Wang_Jin_Xue_Zhu_Choo_2024, title={MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29669}, DOI={10.1609/aaai.v38i15.29669}, abstractNote={To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhu, Zhiyu and Chen, Huaming and Zhang, Jiayu and Wang, Xinyi and Jin, Zhibo and Xue, Minhui and Zhu, Dongxiao and Choo, Kim-Kwang Raymond}, year={2024}, month={Mar.}, pages={17228-17236} }