Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability Through SHAP Value Consistency
Abstract
In this work, we identify continual learning (CL) methods’ inherent differences in sequential decision attribution. In the sequential learning process, inconsistent decision attribution may undermine the interpretability of a continual learner. However, existing CL evaluation metrics, as well as current interpretability methods, cannot measure the decision attribution stability of a continual learner. To bridge the gap, we introduce Shapley value, a well-known decision attribution theory, and define SHAP value consistency (SHAPC) to measure the consistency of a continual learner’s decision attribution. Furthermore, we define the mean and the variance of SHAPC values, namely SHAPC-Mean and SHAPC-Var, to jointly evaluate the decision attribution stability of continual learners over sequential tasks. On Split CIFAR-10, Split CIFAR-100, and Split TinyImageNet, we compare the decision attribution stability of different CL methods using the proposed metrics, providing a new perspective for evaluating their reliability.
Cite
Text
Cai et al. "Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability Through SHAP Value Consistency." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00566Markdown
[Cai et al. "Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability Through SHAP Value Consistency." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/cai2024cvprw-our/) doi:10.1109/CVPRW63382.2024.00566BibTeX
@inproceedings{cai2024cvprw-our,
title = {{Is Our Continual Learner Reliable? Investigating Its Decision Attribution Stability Through SHAP Value Consistency}},
author = {Cai, Yusong and Ling, Shimou and Zhang, Liang and Pan, Lili and Li, Hongliang},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {5568-5575},
doi = {10.1109/CVPRW63382.2024.00566},
url = {https://mlanthology.org/cvprw/2024/cai2024cvprw-our/}
}