LETA: Learning Transferable Attribution for Generic Vision Explainer
Guanchu Wang Yu-Neng Chuang Fan Yang Mengnan Du Chia-Yuan Chang Shaochen Zhong Zirui Liu Zhaozhuo Xu Kaixiong Zhou Xuanting Cai Xia Hu
Explainable machine learning significantly improves the transparency of deep
neural networks~(DNN). However, existing work is constrained to explaining the
behavior of individual model predictions, and lacks the ability to transfer the
explanation across various models and tasks. This limitation results in
explaining various tasks being time- and resource-consuming. To address this
problem, we develop a pre-trained, DNN-based, generic explainer on large-scale
image datasets, and leverage its transferability to explain various vision
models for downstream tasks. In particular, the pre-training of generic
explainer focuses on LEarning Transferable Attribution (LETA). The transferable
attribution takes advantage of the versatile output of the target backbone
encoders to comprehensively encode the essential attribution for explaining
various downstream tasks. LETA guides the pre-training of the generic explainer
towards the transferable attribution, and introduces a rule-based adaptation of
the transferable attribution for explaining downstream tasks, without the need
for additional training on downstream data. Theoretical analysis demonstrates
that the pre-training of LETA enables minimizing the explanation error bound
aligned with the conditional $\mathcal{V}$-information on downstream tasks.
Empirical studies involve explaining three different architectures of vision
models across three diverse downstream datasets. The experiment results
indicate LETA is effective in explaining these tasks without the need for
additional training on the data of downstream tasks.