Suppress and Diversify: Refining Robust Pathways for Corruption Robustness

• URL: http://arxiv.org/abs/2608.06712v1
• Date: Fri, 07 Aug 2026 02:11:59 GMT
• Title: Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
• Abstract summary: Suppress and Diversify (S&D) is a non-intrusive refinement approach that enhances robustness by dynamically selecting robust pathways.<n>S&D is architecture-agnostic, parameter-free, and incurs zero test-time overhead.
• Score: 5.2174037606370725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model robustness against natural image corruptions is essential for safety-critical applications. While existing methods primarily focus on implicit representation learning, we provide the first systematic exploration of computational pathways to explicitly characterize internal robustness. We identify a progressive decay of robust features across network layers and establish a functional dependency between the prevalence of these features and model performance. To exploit these insights, we propose Suppress and Diversify (S\&D), a non-intrusive refinement approach that enhances robustness by dynamically selecting robust pathways and diversifying them through symmetry-preserving transformations. S\&D is architecture-agnostic, parameter-free, and incurs zero test-time overhead. Extensive evaluations across eight benchmarks demonstrate that S\&D consistently improves performance across multiple vision tasks, diverse backbones, and complex real-world scenarios, highlighting its broad efficacy and scalability.

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