Visual prompting, an efficient method for transfer learning, has shown its
potential in vision tasks. However, previous works focus exclusively on VP from
standard source models, it is still unknown how it performs under the scenario
of a robust source model: Whether a visual prompt derived from a robust model
can inherit the robustness while suffering from the generalization performance
decline, albeit for a downstream dataset that is different from the source
dataset? In this work, we get an affirmative answer of the above question and
give an explanation on the visual representation level. Moreover, we introduce
a novel technique named Prompt Boundary Loose (PBL) to effectively mitigates
the suboptimal results of visual prompt on standard accuracy without losing (or
even significantly improving) its adversarial robustness when using a robust
model as source model. Extensive experiments across various datasets show that
our findings are universal and demonstrate the significant benefits of our
proposed method.

我们研究了视觉提示在强大源模型下的性能表现，并提出了一种名为 Prompt Boundary Loose (PBL) 的新技术来有效减轻在标准准确性上的次优结果，同时使用强大模型作为源模型不会丧失（甚至明显改善）其对抗性鲁棒性。在多个数据集上的广泛实验证明了我们发现的泛用性，并展示了我们提出方法的显著优势。