  Explainable machine learning significantly improves the transparency of deep
neural networks. 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 introduce a Transferable Vision Explainer (TVE) that can
effectively explain various vision models in downstream tasks. Specifically,
the transferability of TVE is realized through a pre-training process on
large-scale datasets towards learning the meta-attribution. This
meta-attribution leverages the versatility of generic backbone encoders to
comprehensively encode the attribution knowledge for the input instance, which
enables TVE to seamlessly transfer to explain various downstream tasks, without
the need for training on task-specific data. Empirical studies involve
explaining three different architectures of vision models across three diverse
downstream datasets. The experimental results indicate TVE is effective in
explaining these tasks without the need for additional training on downstream
data.
