- The paper demonstrates that visual prompting inherits the robustness of adversarially trained models while exhibiting a trade-off with generalization accuracy.
- It introduces the Prompt Boundary Loosening (PBL) strategy to expand decision boundaries and boost standard accuracy across various datasets.
- Empirical evidence confirms that robust visual prompting refines feature representations, aligning with human perception in computer vision tasks.
Exploring Visual Prompting: Robustness Inheritance and Beyond
In the domain of computer vision, transferring knowledge efficiently from large-scale pre-trained models to target tasks is paramount. This paper explores Visual Prompting (VP), an innovative technique aimed at optimizing such transfer processes. With VP, the bulk parameters of a pre-trained model remain untouched, while only a minimal, learnable set of inputs is employed. This approach promises reduced computational cost and more seamless integration with diverse domains.
A significant aspect of this research is its exploration of VP in conjunction with robust source models. Robust models, often obtained through adversarial training, inherently stand resilient against adversarial attacks, yet they characteristically suffer from diminished accuracy on standard data. The research addresses how VP leverages the robustness of these models, raising pivotal inquiries: Can VP effectively inherit the robustness of robust models? Does VP encounter similar trade-offs between robustness and generalization as the models from which they derive?
Empirical evidence presented affirms that VP retains the robustness qualities intrinsic to robust models. Nevertheless, it similarly inherits the known trade-off, where robustness often compromises generalization performance. An introduction of the Prompt Boundary Loosening (PBL) strategy is set forth as a remedy. PBL operates as a lightweight, adaptable approach that seamlessly integrates with VP, broadening decision boundaries and thereby enhancing generalization without undermining robustness.
Quantitative validations underscore the universality and effectiveness of PBL across several datasets and robust source models. The experimental results elucidate significant boosts in standard accuracy through PBL, alongside sustained or improved adversarial robustness. Another notable assertion involves the intrinsic visual alignment of robust VP with human perception—a distinguishing trait absent in standard VP—as robust models refine feature representations fundamentally divergent from standard learning models.
This work provides a foundation for further investigation into optimizing transfer learning methodologies within adversarial contexts. The findings and strategies presented herein have significant implications for both theoretical advancements in model training paradigms and practical applications in robust AI systems. Moving forward, extending the scope of VP with adaptive strategies such as PBL might stimulate innovative solutions to perennial challenges in adversarial robust learning, providing scalability and reliability across sectors requiring high-stakes AI deployment.