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
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 axioms of sensitivity
and implementation invariance to ensure the validity and reliability of
attribution results. Yet, the existing attribution methods present challenges
for effective interpretation and efficient computation. In this work, we
introduce MFABA, an attribution algorithm that adheres to axioms, as a novel
method for interpreting DNN. Additionally, we provide the theoretical proof and
in-depth analysis for MFABA algorithm, and conduct a large scale experiment.
The results demonstrate its superiority by achieving over 101.5142 times faster
speed than the state-of-the-art attribution algorithms. The effectiveness of
MFABA is thoroughly evaluated through the statistical analysis in comparison to
other methods, and the full implementation package is open-source at:
https://github.com/LMBTough/MFABA